query: The query consists of users’ needs, leading to several research questions that span a paragraph. Each candidate passage is an abstract from a scientific paper. The objective of this information retrieval task is to identify the abstract that most effectively meets the user’s needs in the query : I am working on a project analyzing customer feedback for a new product launch, so I need a method that can effectively identify the topics and segments in each feedback. Since the feedback we received is very complicated and contains a lot of noise words that digress from main topics, I need a method that is able to remove noise and simplify these feedbacks. Using this method, I hope our system could provide insights into the most common issues customers are facing and help us improve the product accordingly. Some ideas I have right now to simplify feedback and remove noise are to leverage word order information and heuristics. The final resulting system must beat state-of-the-art systems in terms of topic segmentation.

positive:
With the evolution of the cloud and customer centric culture, we inherently
accumulate huge repositories of textual reviews, feedback, and support
data.This has driven enterprises to seek and research engagement patterns, user
network analysis, topic detections, etc.However, huge manual work is still
necessary to mine data to be able to mine actionable outcomes. In this paper,
we proposed and developed an innovative Semi-Supervised Learning approach by
utilizing Deep Learning and Topic Modeling to have a better understanding of
the user voice.This approach combines a BERT-based multiclassification
algorithm through supervised learning combined with a novel Probabilistic and
Semantic Hybrid Topic Inference (PSHTI) Model through unsupervised learning,
aiming at automating the process of better identifying the main topics or areas
as well as the sub-topics from the textual feedback and support.There are three
major break-through: 1. As the advancement of deep learning technology, there
have been tremendous innovations in the NLP field, yet the traditional topic
modeling as one of the NLP applications lag behind the tide of deep learning.
In the methodology and technical perspective, we adopt transfer learning to
fine-tune a BERT-based multiclassification system to categorize the main topics
and then utilize the novel PSHTI model to infer the sub-topics under the
predicted main topics. 2. The traditional unsupervised learning-based topic
models or clustering methods suffer from the difficulty of automatically
generating a meaningful topic label, but our system enables mapping the top
words to the self-help issues by utilizing domain knowledge about the product
through web-crawling. 3. This work provides a prominent showcase by leveraging
the state-of-the-art methodology in the real production to help shed light to
discover user insights and drive business investment priorities.

positive:
This paper deals with the automatic analysis of conversations between a
customer and an agent in a call centre of a customer care service. The purpose
of the analysis is to hypothesize themes about problems and complaints
discussed in the conversation. Themes are defined by the application
documentation topics. A conversation may contain mentions that are irrelevant
for the application purpose and multiple themes whose mentions may be
interleaved portions of a conversation that cannot be well defined. Two methods
are proposed for multiple theme hypothesization. One of them is based on a
cosine similarity measure using a bag of features extracted from the entire
conversation. The other method introduces the concept of thematic density
distributed around specific word positions in a conversation. In addition to
automatically selected words, word bi-grams with possible gaps between
successive words are also considered and selected. Experimental results show
that the results obtained with the proposed methods outperform the results
obtained with support vector machines on the same data. Furthermore, using the
theme skeleton of a conversation from which thematic densities are derived, it
will be possible to extract components of an automatic conversation report to
be used for improving the service performance. Index Terms: multi-topic audio
document classification, hu-man/human conversation analysis, speech analytics,
distance bigrams

positive:
Opinions in forums and social networks are released by millions of people due
to the increasing number of users that use Web 2.0 platforms to opine about
brands and organizations. For enterprises or government agencies it is almost
impossible to track what people say producing a gap between user
needs/expectations and organizations actions. To bridge this gap we create
Viscovery, a platform for opinion summarization and trend tracking that is able
to analyze a stream of opinions recovered from forums. To do this we use
dynamic topic models, allowing to uncover the hidden structure of topics behind
opinions, characterizing vocabulary dynamics. We extend dynamic topic models
for incremental learning, a key aspect needed in Viscovery for model updating
in near-real time. In addition, we include in Viscovery sentiment analysis,
allowing to separate positive/negative words for a specific topic at different
levels of granularity. Viscovery allows to visualize representative opinions
and terms in each topic. At a coarse level of granularity, the dynamic of the
topics can be analyzed using a 2D topic embedding, suggesting longitudinal
topic merging or segmentation. In this paper we report our experience
developing this platform, sharing lessons learned and opportunities that arise
from the use of sentiment analysis and topic modeling in real world
applications.

positive:
A massive amount of reviews are generated daily from various platforms. It is
impossible for people to read through tons of reviews and to obtain useful
information. Automatic summarizing customer reviews thus is important for
identifying and extracting the essential information to help users to obtain
the gist of the data. However, as customer reviews are typically short,
informal, and multifaceted, it is extremely challenging to generate topic-wise
summarization.While there are several studies aims to solve this issue, they
are heuristic methods that are developed only utilizing customer reviews.
Unlike existing method, we propose an effective new summarization method by
analyzing both reviews and summaries.To do that, we first segment reviews and
summaries into individual sentiments. As the sentiments are typically short, we
combine sentiments talking about the same aspect into a single document and
apply topic modeling method to identify hidden topics among customer reviews
and summaries. Sentiment analysis is employed to distinguish positive and
negative opinions among each detected topic. A classifier is also introduced to
distinguish the writing pattern of summaries and that of customer reviews.
Finally, sentiments are selected to generate the summarization based on their
topic relevance, sentiment analysis score and the writing pattern. To test our
method, a new dataset comprising product reviews and summaries about 1028
products are collected from Amazon and CNET. Experimental results show the
effectiveness of our method compared with other methods.

positive:
Understanding and being able to react to customer feedback is the most
fundamental task in providing good customer service. However, there are two
major obstacles for international companies to automatically detect the meaning
of customer feedback in a global multilingual environment. Firstly, there is no
widely acknowledged categorisation (classes) of meaning for customer feedback.
Secondly, the applicability of one meaning categorisation, if it exists, to
customer feedback in multiple languages is questionable. In this paper, we
extracted representative real world samples of customer feedback from Microsoft
Office customers in multiple languages, English, Spanish and Japanese,and
concluded a five-class categorisation(comment, request, bug, complaint and
meaningless) for meaning classification that could be used across languages in
the realm of customer feedback analysis.

positive:
With the development of e-commerce, many products are now being sold
worldwide, and manufacturers are eager to obtain a better understanding of
customer behavior in various regions. To achieve this goal, most previous
efforts have focused mainly on questionnaires, which are time-consuming and
costly. The tremendous volume of product reviews on e-commerce websites has
seen a new trend emerge, whereby manufacturers attempt to understand user
preferences by analyzing online reviews. Following this trend, this paper
addresses the problem of studying customer behavior by exploiting recently
developed opinion mining techniques. This work is novel for three reasons.
First, questionnaire-based investigation is automatically enabled by employing
algorithms for template-based question generation and opinion mining-based
answer extraction. Using this system, manufacturers are able to obtain reports
of customer behavior featuring a much larger sample size, more direct
information, a higher degree of automation, and a lower cost. Second,
international customer behavior study is made easier by integrating tools for
multilingual opinion mining. Third, this is the first time an automatic
questionnaire investigation has been conducted to compare customer behavior in
China and America, where product reviews are written and read in Chinese and
English, respectively. Our study on digital cameras, smartphones, and tablet
computers yields three findings. First, Chinese customers follow the Doctrine
of the Mean, and often use euphemistic expressions, while American customers
express their opinions more directly. Second, Chinese customers care more about
general feelings, while American customers pay more attention to product
details. Third, Chinese customers focus on external features, while American
customers care more about the internal features of products.

positive:
Topic evolution modeling has been researched for a long time and has gained
considerable interest. A state-of-the-art method has been recently using word
modeling algorithms in combination with community detection mechanisms to
achieve better results in a more effective way. We analyse results of this
approach and discuss the two major challenges that this approach still faces.
Although the topics that have resulted from the recent algorithm are good in
general, they are very noisy due to many topics that are very unimportant
because of their size, words, or ambiguity. Additionally, the number of words
defining each topic is too large, making it difficult to analyse them in their
unsorted state. In this paper, we propose approaches to tackle these challenges
by adding topic filtering and network analysis metrics to define the importance
of a topic. We test different combinations of these metrics to see which
combination yields the best results. Furthermore, we add word filtering and
ranking to each topic to identify the words with the highest novelty
automatically. We evaluate our enhancement methods in two ways: human
qualitative evaluation and automatic quantitative evaluation. Moreover, we
created two case studies to test the quality of the clusters and words. In the
quantitative evaluation, we use the pairwise mutual information score to test
the coherency of topics. The quantitative evaluation also includes an analysis
of execution times for each part of the program. The results of the
experimental evaluations show that the two evaluation methods agree on the
positive feasibility of the algorithm. We then show possible extensions in the
form of usability and future improvements to the algorithm.

positive:
Topic modeling is used for discovering latent semantic structure, usually
referred to as topics, in a large collection of documents. The most widely used
methods are Latent Dirichlet Allocation and Probabilistic Latent Semantic
Analysis. Despite their popularity they have several weaknesses. In order to
achieve optimal results they often require the number of topics to be known,
custom stop-word lists, stemming, and lemmatization. Additionally these methods
rely on bag-of-words representation of documents which ignore the ordering and
semantics of words. Distributed representations of documents and words have
gained popularity due to their ability to capture semantics of words and
documents. We present $\texttt{top2vec}$, which leverages joint document and
word semantic embedding to find $\textit{topic vectors}$. This model does not
require stop-word lists, stemming or lemmatization, and it automatically finds
the number of topics. The resulting topic vectors are jointly embedded with the
document and word vectors with distance between them representing semantic
similarity. Our experiments demonstrate that $\texttt{top2vec}$ finds topics
which are significantly more informative and representative of the corpus
trained on than probabilistic generative models.

positive:
The application and usage of opinion mining, especially for business
intelligence, product recommendation, targeted marketing etc. have fascinated
many research attentions around the globe. Various research efforts attempted
to mine opinions from customer reviews at different levels of granularity,
including word-, sentence-, and document-level. However, development of a fully
automatic opinion mining and sentiment analysis system is still elusive. Though
the development of opinion mining and sentiment analysis systems are getting
momentum, most of them attempt to perform document-level sentiment analysis,
classifying a review document as positive, negative, or neutral. Such
document-level opinion mining approaches fail to provide insight about users
sentiment on individual features of a product or service. Therefore, it seems
to be a great help for both customers and manufacturers, if the reviews could
be processed at a finer-grained level and presented in a summarized form
through some visual means, highlighting individual features of a product and
users sentiment expressed over them. In this paper, the design of a unified
opinion mining and sentiment analysis framework is presented at the
intersection of both machine learning and natural language processing
approaches. Also, design of a novel feature-level review summarization scheme
is proposed to visualize mined features, opinions and their polarity values in
a comprehendible way.

positive:
A new opinion extraction method is proposed to summarize unstructured,
user-generated content (i.e., online customer reviews) in the fixed topic
domains. To differentiate the current approach from other opinion extraction
approaches, which are often exposed to a sparsity problem and lack of sentiment
scores, a confirmatory aspect-based opinion mining framework is introduced
along with its practical algorithm called DiSSBUS. In this procedure, 1) each
customer review is disintegrated into a set of clauses; 2) each clause is
summarized to bi-terms-a topic word and an evaluation word-using a
part-of-speech (POS) tagger; and 3) each bi-term is matched to a pre-specified
topic relevant to a specific domain. The proposed processes have two primary
advantages over existing methods: 1) they can decompose a single review into a
set of bi-terms related to pre-specified topics in the domain of interest and,
therefore, 2) allow identification of the reviewer's opinions on the topics via
evaluation words within the set of bi-terms. The proposed aspect-based opinion
mining is applied to customer reviews of restaurants in Hawaii obtained from
TripAdvisor, and the empirical findings validate the effectiveness of the
method.
  Keywords: Clause-based sentiment analysis, Customer review, Opinion mining,
Topic modeling, User-generate-contents.

negative:
With the increasing number of merchandise on e-commerce platforms, users tend
to refer to reviews of other shoppers to decide which product they should buy.
However, with so many reviews of a product, users often have to spend lots of
time browsing through reviews talking about product attributes they do not care
about. We want to establish a system that can automatically summarize and
answer user's product specific questions.
  In this study, we propose a novel seq2seq based text generation model to
generate answers to user's question based on reviews posted by previous users.
Given a user question and/or target sentiment polarity, we extract aspects of
interest and generate an answer that summarizes previous relevant user reviews.
Specifically, our model performs attention between input reviews and target
aspects during encoding and is conditioned on both review rating and input
context during decoding. We also incorporate a pre-trained auxiliary rating
classifier to improve model performance and accelerate convergence during
training. Experiments using real-world e-commerce dataset show that our model
achieves improvement in performance compared to previously introduced models.

negative:
In order to better engage with customers, retailers rely on extensive
customer and product databases which allows them to better understand customer
behaviour and purchasing patterns. This has long been a challenging task as
customer modelling is a multi-faceted, noisy and time-dependent problem. The
most common way to tackle this problem is indirectly through task-specific
supervised learning prediction problems, with relatively little literature on
modelling a customer by directly simulating their future transactions. In this
paper we propose a method for generating realistic sequences of baskets that a
given customer is likely to purchase over a period of time. Customer embedding
representations are learned using a Recurrent Neural Network (RNN) which takes
into account the entire sequence of transaction data. Given the customer state
at a specific point in time, a Generative Adversarial Network (GAN) is trained
to generate a plausible basket of products for the following week. The newly
generated basket is then fed back into the RNN to update the customer's state.
The GAN is thus used in tandem with the RNN module in a pipeline alternating
between basket generation and customer state updating steps. This allows for
sampling over a distribution of a customer's future sequence of baskets, which
then can be used to gain insight into how to service the customer more
effectively. The methodology is empirically shown to produce baskets that
appear similar to real baskets and enjoy many common properties, including
frequencies of different product types, brands, and prices. Furthermore, the
generated data is able to replicate most of the strongest sequential patterns
that exist between product types in the real data.

negative:
Text mining approaches are being used increasingly for business analytics. In
particular, such approaches are now central to understanding users' feedback
regarding systems delivered via online application distribution platforms such
as Google Play. In such settings, large volumes of reviews of potentially
numerous apps and systems means that it is infeasible to use manual mechanisms
to extract insights and knowledge that could inform product improvement. In
this context of identifying software system improvement options, text mining
techniques are used to reveal the features that are mentioned most often as
being in need of correction (e.g., GPS), and topics that are associated with
features perceived as being defective (e.g., inaccuracy of GPS). Other
approaches may supplement such techniques to provide further insights for
online communities and solution providers. In this work we augment text mining
approaches with social network analysis to demonstrate the utility of using
multiple techniques. Our outcomes suggest that text mining approaches may
indeed be supplemented with other methods to deliver a broader range of
insights.

negative:
Commercial establishments like restaurants, service centres and retailers
have several sources of customer feedback about products and services, most of
which need not be as structured as rated reviews provided by services like
Yelp, or Amazon, in terms of sentiment conveyed. For instance, Amazon provides
a fine-grained score on a numeric scale for product reviews. Some sources,
however, like social media (Twitter, Facebook), mailing lists (Google Groups)
and forums (Quora) contain text data that is much more voluminous, but
unstructured and unlabelled. It might be in the best interests of a business
establishment to assess the general sentiment towards their brand on these
platforms as well. This text could be pipelined into a system with a built-in
prediction model, with the objective of generating real-time graphs on opinion
and sentiment trends. Although such tasks like the one described about have
been explored with respect to document classification problems in the past, the
implementation described in this paper, by virtue of learning a continuous
function rather than a discrete one, offers a lot more depth of insight as
compared to document classification approaches. This study aims to explore the
validity of such a continuous function predicting model to quantify sentiment
about an entity, without the additional overhead of manual labelling, and
computational preprocessing & feature extraction. This research project also
aims to design and implement a re-usable document regression pipeline as a
framework, Rapid-Rate, that can be used to predict document scores in
real-time.

negative:
Customers post online reviews at any time. With the timestamp of online
reviews, they can be regarded as a flow of information. With this
characteristic, designers can capture the changes in customer feedback to help
set up product improvement strategies. Here we propose an approach for
capturing changes of user expectation on product affordances based on the
online reviews for two generations of products. First, the approach uses a
rule-based natural language processing method to automatically identify and
structure product affordances from review text. Then, inspired by the Kano
model which classifies preferences of product attributes in five categories,
conjoint analysis is used to quantitatively categorize the structured
affordances. Finally, changes of user expectation can be found by applying the
conjoint analysis on the online reviews posted for two successive generations
of products. A case study based on the online reviews of Kindle e-readers
downloaded from amazon.com shows that designers can use our proposed approach
to evaluate their product improvement strategies for previous products and
develop new product improvement strategies for future products.

negative:
We address two challenges in topic models: (1) Context information around
words helps in determining their actual meaning, e.g., "networks" used in the
contexts "artificial neural networks" vs. "biological neuron networks".
Generative topic models infer topic-word distributions, taking no or only
little context into account. Here, we extend a neural autoregressive topic
model to exploit the full context information around words in a document in a
language modeling fashion. The proposed model is named as iDocNADE. (2) Due to
the small number of word occurrences (i.e., lack of context) in short text and
data sparsity in a corpus of few documents, the application of topic models is
challenging on such texts. Therefore, we propose a simple and efficient way of
incorporating external knowledge into neural autoregressive topic models: we
use embeddings as a distributional prior. The proposed variants are named as
DocNADEe and iDocNADEe.
  We present novel neural autoregressive topic model variants that consistently
outperform state-of-the-art generative topic models in terms of generalization,
interpretability (topic coherence) and applicability (retrieval and
classification) over 7 long-text and 8 short-text datasets from diverse
domains.

negative:
One of the biggest hurdles for customers when purchasing fashion online, is
the difficulty of finding products with the right fit. In order to provide a
better online shopping experience, platforms need to find ways to recommend the
right product sizes and the best fitting products to their customers. These
recommendation systems, however, require customer feedback in order to estimate
the most suitable sizing options. Such feedback is rare and often only
available as natural text. In this paper, we examine the extraction of product
fit feedback from customer reviews using natural language processing
techniques. In particular, we compare traditional methods with more recent
transfer learning techniques for text classification, and analyze their
results. Our evaluation shows, that the transfer learning approach ULMFit is
not only comparatively fast to train, but also achieves highest accuracy on
this task. The integration of the extracted information with actual size
recommendation systems is left for future work.

negative:
Online product reviews are a valuable resource for product developers to
improve the design of their products. Yet, the potential value of customer
feedback to improve the sustainability performance of products is still to be
exploited. The present paper investigates and analyzes Amazon product reviews
to bring new light on the following question: ``What sustainable design
insights can be identified or interpreted from online product reviews?''. To do
so, the top 100 reviews, evenly distributed by star ratings, for three product
categories (laptop, printer, cable) are collected, manually annotated, analyzed
and interpreted. For each product category, the reviews of two similar products
(one with environmental certification and one standard version) are compared
and combined to come up with sustainable design solutions. In all, for the six
products considered, between 12% and 20% of the reviews mentioned directly or
indirectly aspects or attributes that could be exploited to improve the design
of these products from a sustainability perspective. Concrete examples of
sustainable design leads that could be elicited from product reviews are given
and discussed. As such, this contribution provides a baseline for future work
willing to automate this process to gain further insights from online product
reviews. Notably, the deployment of machine learning tools and the use of
natural language processing techniques to do so are discussed as promising
lines for future research.

negative:
In this paper, we propose two automated text processing frameworks
specifically designed to analyze online reviews. The objective of the first
framework is to summarize the reviews dataset by extracting essential sentence.
This is performed by converting sentences into numerical vectors and clustering
them using a community detection algorithm based on their similarity levels.
Afterwards, a correlation score is measured for each sentence to determine its
importance level in each cluster and assign it as a tag for that community. The
second framework is based on a question-answering neural network model trained
to extract answers to multiple different questions. The collected answers are
effectively clustered to find multiple distinct answers to a single question
that might be asked by a customer. The proposed frameworks are shown to be more
comprehensive than existing reviews processing solutions.

negative:
Topic modeling is commonly used to analyze and understand large document
collections. However, in practice, users want to focus on specific aspects or
"targets" rather than the entire corpus. For example, given a large collection
of documents, users may want only a smaller subset which more closely aligns
with their interests, tasks, and domains. In particular, our paper focuses on
large-scale document retrieval with high recall where any missed relevant
documents can be critical. A simple keyword matching search is generally not
effective nor efficient as 1) it is difficult to find a list of keyword queries
that can cover the documents of interest before exploring the dataset, 2) some
documents may not contain the exact keywords of interest but may still be
highly relevant, and 3) some words have multiple meanings, which would result
in irrelevant documents included in the retrieved subset. In this paper, we
present TopicSifter, a visual analytics system for interactive search space
reduction. Our system utilizes targeted topic modeling based on nonnegative
matrix factorization and allows users to give relevance feedback in order to
refine their target and guide the topic modeling to the most relevant results.

negative:
Mobile apps are one of the building blocks of the mobile digital economy. A
differentiating feature of mobile apps to traditional enterprise software is
online reviews, which are available on app marketplaces and represent a
valuable source of consumer feedback on the app. We create a supervised topic
modeling approach for app developers to use mobile reviews as useful sources of
quality and customer feedback, thereby complementing traditional software
testing. The approach is based on a constrained matrix factorization that
leverages the relationship between term frequency and a given response variable
in addition to co-occurrences between terms to recover topics that are both
predictive of consumer sentiment and useful for understanding the underlying
textual themes. The factorization is combined with ordinal regression to
provide guidance from online reviews on a single app's performance as well as
systematically compare different apps over time for benchmarking of features
and consumer sentiment. We apply our approach using a dataset of over 100,000
mobile reviews over several years for three of the most popular online travel
agent apps from the iTunes and Google Play marketplaces.

negative:
E-commerce stores collect customer feedback to let sellers learn about
customer concerns and enhance customer order experience. Because customer
feedback often contains redundant information, a concise summary of the
feedback can be generated to help sellers better understand the issues causing
customer dissatisfaction. Previous state-of-the-art abstractive text
summarization models make two major types of factual errors when producing
summaries from customer feedback, which are wrong entity detection (WED) and
incorrect product-defect description (IPD). In this work, we introduce a set of
methods to enhance the factual consistency of abstractive summarization on
customer feedback. We augment the training data with artificially corrupted
summaries, and use them as counterparts of the target summaries. We add a
contrastive loss term into the training objective so that the model learns to
avoid certain factual errors. Evaluation results show that a large portion of
WED and IPD errors are alleviated for BART and T5. Furthermore, our approaches
do not depend on the structure of the summarization model and thus are
generalizable to any abstractive summarization systems.

negative:
Today's businesses face a high pressure to innovate in order to succeed in
highly competitive markets. Successful innovations, though, typically require
the identification and analysis of customer needs. While traditional,
established need elicitation methods are time-proven and have demonstrated
their capabilities to deliver valuable insights, they lack automation and
scalability and, thus, are expensive and time-consuming. In this article, we
propose an approach to automatically identify and quantify customer needs by
utilizing a novel data source: Users voluntarily and publicly expose
information about themselves via social media, as for instance Facebook or
Twitter. These posts may contain valuable information about the needs, wants,
and demands of their authors. We apply a Design Science Research (DSR)
methodology to add design knowledge and artifacts for the digitalization of
innovation processes, in particular to provide digital support for the
elicitation of customer needs. We want to investigate whether automated,
speedy, and scalable need elicitation from social media is feasible. We
concentrate on Twitter as a data source and on e-mobility as an application
domain. In a first design cycle we conceive, implement and evaluate a method to
demonstrate the feasibility of identifying those social media posts that
actually express customer needs. In a second cycle, we build on this artifact
to additionally quantify the need information elicited, and prove its
feasibility. Third, we integrate both developed methods into an end-user
software artifact and test usability in an industrial use case. Thus, we add
new methods for need elicitation to the body of knowledge, and introduce
concrete tooling for innovation management in practice.

negative:
Auto dealerships receive thousands of calls daily from customers who are
interested in sales, service, vendors and jobseekers. With so many calls, it is
very important for auto dealers to understand the intent of these calls to
provide positive customer experiences that ensure customer satisfaction, deep
customer engagement to boost sales and revenue, and optimum allocation of
agents or customer service representatives across the business. In this paper,
we define the problem of customer phone call intent as a multi-class
classification problem stemming from the large database of recorded phone call
transcripts. To solve this problem, we develop a convolutional neural network
(CNN)-based supervised learning model to classify the customer calls into four
intent categories: sales, service, vendor and jobseeker. Experimental results
show that with the thrust of our scalable data labeling method to provide
sufficient training data, the CNN-based predictive model performs very well on
long text classification according to the quantitative metrics of F1-Score,
precision, recall, and accuracy.

negative:
Topical Segmentation poses a great role in reducing search space of the
topics taught in a lecture video specially when the video metadata lacks topic
wise segmentation information. This segmentation information eases user efforts
of searching, locating and browsing a topic inside a lecture video. In this
work we propose an algorithm, that combines state-of-the art language model and
domain knowledge graph for automatically detecting different coherent topics
present inside a long lecture video. We use the language model on
speech-to-text transcription to capture the implicit meaning of the whole video
while the knowledge graph provides us the domain specific dependencies between
different concepts of that subjects. Also leveraging the domain knowledge we
can capture the way instructor binds and connects different concepts while
teaching, which helps us in achieving better segmentation accuracy. We tested
our approach on NPTEL lecture videos and holistic evaluation shows that it out
performs the other methods described in the literature.

negative:
Social networking sites (SNSs) facilitate the sharing of ideas and
information through different types of feedback including publishing posts,
leaving comments and other type of reactions. However, some comments or
feedback on SNSs are inconsiderate and offensive, and sometimes this type of
feedback has a very negative effect on a target user. The phenomenon known as
flaming goes hand-in-hand with this type of posting that can trigger almost
instantly on SNSs. Most popular users such as celebrities, politicians and news
media are the major victims of the flaming behaviors and so detecting these
types of events will be useful and appreciated. Flaming event can be monitored
and identified by analyzing negative comments received on a post. Thus, our
main objective of this study is to identify a way to detect flaming events in
SNS using a sentiment prediction method. We use a deep Neural Network (NN)
model that can identity sentiments of variable length sentences and classifies
the sentiment of SNSs content (both comments and posts) to discover flaming
events. Our deep NN model uses Word2Vec and FastText word embedding methods as
its training to explore which method is the most appropriate. The labeled
dataset for training the deep NN is generated using an enhanced lexicon based
approach. Our deep NN model classifies the sentiment of a sentence into five
classes: Very Positive, Positive, Neutral, Negative and Very Negative. To
detect flaming incidents, we focus only on the comments classified into the
Negative and Very Negative classes. As a use-case, we try to explore the
flaming phenomena in the news media domain and therefore we focused on news
items posted by three popular news media on Facebook (BBCNews, CNN and FoxNews)
to train and test the model.

negative:
The most important goal of customer services is to keep the customer
satisfied. However, service resources are always limited and must be
prioritized. Therefore, it is important to identify customers who potentially
become unsatisfied and might lead to escalations. Today this prioritization of
customers is often done manually. Data science on IoT data (esp. log data) for
machine health monitoring, as well as analytics on enterprise data for customer
relationship management (CRM) have mainly been researched and applied
independently. In this paper, we present a framework for a data-driven decision
support system which combines IoT and enterprise data to model customer
sentiment. Such decision support systems can help to prioritize customers and
service resources to effectively troubleshoot problems or even avoid them. The
framework is applied in a real-world case study with a major medical device
manufacturer. This includes a fully automated and interpretable machine
learning pipeline designed to meet the requirements defined with domain experts
and end users. The overall framework is currently deployed, learns and
evaluates predictive models from terabytes of IoT and enterprise data to
actively monitor the customer sentiment for a fleet of thousands of high-end
medical devices. Furthermore, we provide an anonymized industrial benchmark
dataset for the research community.

negative:
Requirements elicitation has recently been complemented with crowd-based
techniques, which continuously involve large, heterogeneous groups of users who
express their feedback through a variety of media. Crowd-based elicitation has
great potential for engaging with (potential) users early on but also results
in large sets of raw and unstructured feedback. Consolidating and analyzing
this feedback is a key challenge for turning it into sensible user
requirements. In this paper, we focus on topic modeling as a means to identify
topics within a large set of crowd-generated user stories and compare three
approaches: (1) a traditional approach based on Latent Dirichlet Allocation,
(2) a combination of word embeddings and principal component analysis, and (3)
a combination of word embeddings and Word Mover's Distance. We evaluate the
approaches on a publicly available set of 2,966 user stories written and
categorized by crowd workers. We found that a combination of word embeddings
and Word Mover's Distance is most promising. Depending on the word embeddings
we use in our approaches, we manage to cluster the user stories in two ways:
one that is closer to the original categorization and another that allows new
insights into the dataset, e.g. to find potentially new categories.
Unfortunately, no measure exists to rate the quality of our results
objectively. Still, our findings provide a basis for future work towards
analyzing crowd-sourced user stories.

negative:
Topic segmentation and labeling is often considered a prerequisite for
higher-level conversation analysis and has been shown to be useful in many
Natural Language Processing (NLP) applications. We present two new corpora of
email and blog conversations annotated with topics, and evaluate annotator
reliability for the segmentation and labeling tasks in these asynchronous
conversations. We propose a complete computational framework for topic
segmentation and labeling in asynchronous conversations. Our approach extends
state-of-the-art methods by considering a fine-grained structure of an
asynchronous conversation, along with other conversational features by applying
recent graph-based methods for NLP. For topic segmentation, we propose two
novel unsupervised models that exploit the fine-grained conversational
structure, and a novel graph-theoretic supervised model that combines lexical,
conversational and topic features. For topic labeling, we propose two novel
(unsupervised) random walk models that respectively capture conversation
specific clues from two different sources: the leading sentences and the
fine-grained conversational structure. Empirical evaluation shows that the
segmentation and the labeling performed by our best models beat the
state-of-the-art, and are highly correlated with human annotations.

negative:
Product recommendation systems are important for major movie studios during
the movie greenlight process and as part of machine learning personalization
pipelines. Collaborative Filtering (CF) models have proved to be effective at
powering recommender systems for online streaming services with explicit
customer feedback data. CF models do not perform well in scenarios in which
feedback data is not available, in cold start situations like new product
launches, and situations with markedly different customer tiers (e.g., high
frequency customers vs. casual customers). Generative natural language models
that create useful theme-based representations of an underlying corpus of
documents can be used to represent new product descriptions, like new movie
plots. When combined with CF, they have shown to increase the performance in
cold start situations. Outside of those cases though in which explicit customer
feedback is available, recommender engines must rely on binary purchase data,
which materially degrades performance. Fortunately, purchase data can be
combined with product descriptions to generate meaningful representations of
products and customer trajectories in a convenient product space in which
proximity represents similarity. Learning to measure the distance between
points in this space can be accomplished with a deep neural network that trains
on customer histories and on dense vectorizations of product descriptions. We
developed a system based on Collaborative (Deep) Metric Learning (CML) to
predict the purchase probabilities of new theatrical releases. We trained and
evaluated the model using a large dataset of customer histories, and tested the
model for a set of movies that were released outside of the training window.
Initial experiments show gains relative to models that do not train on
collaborative preferences.

negative:
We address two challenges of probabilistic topic modelling in order to better
estimate the probability of a word in a given context, i.e., P(word|context):
(1) No Language Structure in Context: Probabilistic topic models ignore word
order by summarizing a given context as a "bag-of-word" and consequently the
semantics of words in the context is lost. The LSTM-LM learns a vector-space
representation of each word by accounting for word order in local collocation
patterns and models complex characteristics of language (e.g., syntax and
semantics), while the TM simultaneously learns a latent representation from the
entire document and discovers the underlying thematic structure. We unite two
complementary paradigms of learning the meaning of word occurrences by
combining a TM (e.g., DocNADE) and a LM in a unified probabilistic framework,
named as ctx-DocNADE. (2) Limited Context and/or Smaller training corpus of
documents: In settings with a small number of word occurrences (i.e., lack of
context) in short text or data sparsity in a corpus of few documents, the
application of TMs is challenging. We address this challenge by incorporating
external knowledge into neural autoregressive topic models via a language
modelling approach: we use word embeddings as input of a LSTM-LM with the aim
to improve the word-topic mapping on a smaller and/or short-text corpus. The
proposed DocNADE extension is named as ctx-DocNADEe.
  We present novel neural autoregressive topic model variants coupled with
neural LMs and embeddings priors that consistently outperform state-of-the-art
generative TMs in terms of generalization (perplexity), interpretability (topic
coherence) and applicability (retrieval and classification) over 6 long-text
and 8 short-text datasets from diverse domains.

negative:
Analyzing customer feedback is the best way to channelize the data into new
marketing strategies that benefit entrepreneurs as well as customers. Therefore
an automated system which can analyze the customer behavior is in great demand.
Users may write feedbacks in any language, and hence mining appropriate
information often becomes intractable. Especially in a traditional
feature-based supervised model, it is difficult to build a generic system as
one has to understand the concerned language for finding the relevant features.
In order to overcome this, we propose deep Convolutional Neural Network (CNN)
and Recurrent Neural Network (RNN) based approaches that do not require
handcrafting of features. We evaluate these techniques for analyzing customer
feedback sentences in four languages, namely English, French, Japanese and
Spanish. Our empirical analysis shows that our models perform well in all the
four languages on the setups of IJCNLP Shared Task on Customer Feedback
Analysis. Our model achieved the second rank in French, with an accuracy of
71.75% and third ranks for all the other languages.

negative:
Improving the energy efficiency of mobile applications is a topic that has
gained a lot of attention recently. It has been addressed in a number of ways
such as identifying energy bugs and developing a catalog of energy patterns.
Previous work shows that users discuss the battery-related issues (energy
inefficiency or energy consumption) of the apps in their reviews. However,
there is no work that addresses the automatic extraction of battery-related
issues from users' feedback. In this paper, we report on a visualization tool
that is developed to empirically study machine learning algorithms and text
features to automatically identify the energy consumption specific reviews with
the highest accuracy. Other than the common machine learning algorithms, we
utilize deep learning models with different word embeddings to compare the
results. Furthermore, to help the developers extract the main topics that are
discussed in the reviews, two states of the art topic modeling algorithms are
applied. The visualizations of the topics represent the keywords that are
extracted for each topic along with a comparison with the results of string
matching. The developed web-browser based interactive visualization tool is a
novel framework developed with the intention of giving the app developers
insights about running time and accuracy of machine learning and deep learning
models as well as extracted topics. The tool makes it easier for the developers
to traverse through the extensive result set generated by the text
classification and topic modeling algorithms. The dynamic-data structure used
for the tool stores the baseline-results of the discussed approaches and is
updated when applied on new datasets. The tool is open-sourced to replicate the
research results.

negative:
Business Processes, i.e., a set of coordinated tasks and activities to
achieve a business goal, and their continuous improvements are key to the
operation of any organization. In banking, business processes are increasingly
dynamic as various technologies have made dynamic processes more prevalent. For
example, customer segmentation, i.e., the process of grouping related customers
based on common activities and behaviors, could be a data-driven and
knowledge-intensive process. In this paper, we present an intelligent
data-driven pipeline composed of a set of processing elements to move
customers' data from one system to another, transforming the data into the
contextualized data and knowledge along the way. The goal is to present a novel
intelligent customer segmentation process which automates the feature
engineering, i.e., the process of using (banking) domain knowledge to extract
features from raw data via data mining techniques, in the banking domain. We
adopt a typical scenario for analyzing customer transaction records, to
highlight how the presented approach can significantly improve the quality of
risk-based customer segmentation in the absence of feature engineering.As
result, our proposed method is able to achieve accuracy of 91% compared to
classical approaches in terms of detecting, identifying and classifying
transaction to the right classification.

negative:
Understanding product attributes plays an important role in improving online
shopping experience for customers and serves as an integral part for
constructing a product knowledge graph. Most existing methods focus on
attribute extraction from text description or utilize visual information from
product images such as shape and color. Compared to the inputs considered in
prior works, a product image in fact contains more information, represented by
a rich mixture of words and visual clues with a layout carefully designed to
impress customers. This work proposes a more inclusive framework that fully
utilizes these different modalities for attribute extraction. Inspired by
recent works in visual question answering, we use a transformer based sequence
to sequence model to fuse representations of product text, Optical Character
Recognition (OCR) tokens and visual objects detected in the product image. The
framework is further extended with the capability to extract attribute value
across multiple product categories with a single model, by training the decoder
to predict both product category and attribute value and conditioning its
output on product category. The model provides a unified attribute extraction
solution desirable at an e-commerce platform that offers numerous product
categories with a diverse body of product attributes. We evaluated the model on
two product attributes, one with many possible values and one with a small set
of possible values, over 14 product categories and found the model could
achieve 15% gain on the Recall and 10% gain on the F1 score compared to
existing methods using text-only features.

negative:
Efficient and appropriate online customer service is essential to large
e-commerce businesses. Existing solution recommendation methods for online
customer service are unable to determine the best solutions at runtime, leading
to poor satisfaction of end customers. This paper proposes a novel intelligent
framework, called ICS-Assist, to recommend suitable customer service solutions
for service staff at runtime. Specifically, we develop a generalizable
two-stage machine learning model to identify customer service scenarios and
determine customer service solutions based on a scenario-solution mapping
table. We implement ICS-Assist and evaluate it using an over 6-month field
study with Alibaba Group. In our experiment, over 12,000 customer service staff
use ICS-Assist to serve for over 230,000 cases per day on average. The
experimen-tal results show that ICS-Assist significantly outperforms the
traditional manual method, and improves the solution acceptance rate, the
solution coverage rate, the average service time, the customer satisfaction
rate, and the business domain catering rate by up to 16%, 25%, 6%, 14% and 17%
respectively, compared to the state-of-the-art methods.

negative:
Accurate user interest modeling is important for news recommendation. Most
existing methods for news recommendation rely on implicit feedbacks like click
for inferring user interests and model training. However, click behaviors
usually contain heavy noise, and cannot help infer complicated user interest
such as dislike. Besides, the feed recommendation models trained solely on
click behaviors cannot optimize other objectives such as user engagement. In
this paper, we present a news feed recommendation method that can exploit
various kinds of user feedbacks to enhance both user interest modeling and
model training. We propose a unified user modeling framework to incorporate
various explicit and implicit user feedbacks to infer both positive and
negative user interests. In addition, we propose a strong-to-weak attention
network that uses the representations of stronger feedbacks to distill positive
and negative user interests from implicit weak feedbacks for accurate user
interest modeling. Besides, we propose a multi-feedback model training
framework to learn an engagement-aware feed recommendation model. Extensive
experiments on a real-world dataset show that our approach can effectively
improve the model performance in terms of both news clicks and user engagement.

negative:
Understanding choices performed by online customers is a growing need in the
travel industry. In many practical situations, the only available information
is the flight search query performed by the customer with no additional profile
knowledge. In general, customer flight bookings are driven by prices, duration,
number of connections, and so on. However, not all customers might assign the
same importance to each of those criteria. Here comes the need of grouping
together all flight searches performed by the same kind of customer, that is
having the same booking criteria. The effectiveness of some set of
recommendations, for a single cluster, can be measured in terms of the number
of bookings historically performed. This effectiveness measure plays the role
of a feedback, that is an external knowledge which can be recombined to
iteratively obtain a final segmentation. In this paper, we describe our Online
Travel Agencies (OTA) flight search use case and highlight its specific
features. We address the flight search segmentation problem motivated above by
proposing a novel algorithm called Split-or-Merge (S/M). This algorithm is a
variation of the Split-Merge-Evolve (SME) method. The SME method has already
been introduced in the community as an iterative process updating a clustering
given by the K-means algorithm by splitting and merging clusters subject to
feedback independent evaluations. No previous application of the SME method to
the real-word data is reported in literature to the best of our knowledge.
Here, we provide experimental evaluations over real-world data to the SME and
the S/M methods. The impact on our domain-specific metrics obtained under the
SME and the S/M methods suggests that feedback clustering techniques can be
very promising in the handling of the domain of OTA flight searches.

negative:
Current topic models often suffer from discovering topics not matching human
intuition, unnatural switching of topics within documents and high
computational demands. We address these concerns by proposing a topic model and
an inference algorithm based on automatically identifying characteristic
keywords for topics. Keywords influence topic-assignments of nearby words. Our
algorithm learns (key)word-topic scores and it self-regulates the number of
topics. Inference is simple and easily parallelizable. Qualitative analysis
yields comparable results to state-of-the-art models (eg. LDA), but with
different strengths and weaknesses. Quantitative analysis using 9 datasets
shows gains in terms of classification accuracy, PMI score, computational
performance and consistency of topic assignments within documents, while most
often using less topics.

negative:
Crowdsourced design feedback systems are emerging resources for getting large
amounts of feedback in a short period of time. Traditionally, the feedback
comes in the form of a declarative statement, which often contains positive or
negative sentiment. Prior research has shown that overly negative or positive
sentiment can strongly influence the perceived usefulness and acceptance of
feedback and, subsequently, lead to ineffective design revisions. To enhance
the effectiveness of crowdsourced design feedback, we investigate a new
approach for mitigating the effects of negative or positive feedback by
combining open-ended and thought-provoking questions with declarative feedback
statements. We conducted two user studies to assess the effects of
question-based feedback on the sentiment and quality of design revisions in the
context of graphic design. We found that crowdsourced question-based feedback
contains more neutral sentiment than statement-based feedback. Moreover, we
provide evidence that presenting feedback as questions followed by statements
leads to better design revisions than question- or statement-based feedback
alone.

negative:
Currently, while software engineers write code for various modules, quite
often, various types of errors - coding, logic, semantic, and others (most of
which are not caught by compilation and other tools) get introduced. Some of
these bugs might be found in the later stage of testing, and many times it is
reported by customers on production code. Companies have to spend many
resources, both money and time in finding and fixing the bugs which would have
been avoided if coding was done right. Also, concealed flaws in software can
lead to security vulnerabilities that potentially allow attackers to compromise
systems and applications. Interestingly, same or similar issues/bugs, which
were fixed in the past (although in different modules), tend to get introduced
in production code again.
  We developed a novel AI-based system which uses the deep representation of
Abstract Syntax Tree (AST) created from the source code and also the active
feedback loop to identify and alert the potential bugs that could be caused at
the time of development itself i.e. as the developer is writing new code (logic
and/or function). This tool integrated with IDE as a plugin would work in the
background, point out existing similar functions/code-segments and any
associated bugs in those functions. The tool would enable the developer to
incorporate suggestions right at the time of development, rather than waiting
for UT/QA/customer to raise a defect.
  We assessed our tool on both open-source code and also on Cisco codebase for
C and C++ programing language. Our results confirm that deep representation of
source code and the active feedback loop is an assuring approach for predicting
security and other vulnerabilities present in the code.

negative:
Customer Segmentation is the process of dividing customers into groups based
on common characteristics. An intelligent Customer Segmentation will not only
enable an organization to effectively allocate marketing resources (e.g.,
Recommender Systems in the Banking sector) but also it will enable identifying
the customer cohorts that are most likely to benefit from a specific policy
(e.g., to discover diverse patient groups in the Health sector). While there
has been a significant improvement in approaches to Customer Segmentation, the
main challenge remains to be the understanding of the reasons behind the
segmentation need. This task is challenging as it is subjective and depends on
the goal of segmentation as well as the analyst's perspective. To address this
challenge, in this paper, we present an intelligent vector-based customer
segmentation approach. The proposed approach will leverage feature engineering
to enable analysts to identify important features (from a pool of features such
as demographics, geography, psychographics, behavioral, and more) and feed them
into a neural embedding framework named Customer2Vec. The Customer2Vec combines
the neural network classification and clustering methods as supervised and
unsupervised learning techniques to embed the customer vector. We adopt a
typical scenario in the Banking Sector to highlight how Customer2Vec
significantly improves the quality of the segmentation and detecting customer
similarities.

negative:
Product feature recommendations are critical for online customers to purchase
the right products based on the right features. For a customer, selecting the
product that has the best trade-off between price and functionality is a
time-consuming step in an online shopping experience, and customers can be
overwhelmed by the available choices. However, determining the set of product
features that most differentiate a particular product is still an open question
in online recommender systems. In this paper, we focus on using interpretable
machine learning methods to tackle this problem. First, we identify this unique
product feature recommendation problem from a business perspective on a major
US e-commerce site. Second, we formulate the problem into a price-driven
supervised learning problem to discover the product features that could best
explain the price of a product in a given product category. We build machine
learning models with a model-agnostic method Shapley Values to understand the
importance of each feature, rank and recommend the most essential features.
Third, we leverage human experts to evaluate its relevancy. The results show
that our method is superior to a strong baseline method based on customer
behavior and significantly boosts the coverage by 45%. Finally, our proposed
method shows comparable conversion rate against the baseline in online A/B
tests.

negative:
Decision makers need economical information to drive their decisions. The
Company Actualis SARL is specialized in the production and distribution of a
press review about French regional economic actors. This economic review
represents for a client a prospecting tool on partners and competitors. To
reduce the overload of useless information, the company is moving towards a
customized review for each customer. Three issues appear to achieve this goal.
First, how to identify the elements in the text in order to extract objects
that match with the recommendation's criteria presented? Second, How to define
the structure of these objects, relationships and articles in order to provide
a source of knowledge usable by the extraction process to produce new knowledge
from articles? The latter issue is the feedback on customer experience to
identify the quality of distributed information in real-time and to improve the
relevance of the recommendations. This paper presents a new type of
recommendation based on the semantic description of both articles and user
profile.

negative:
In marketing we are often confronted with a continuous stream of responses to
marketing messages. Such streaming data provide invaluable information
regarding message effectiveness and segmentation. However, streaming data are
hard to analyze using conventional methods: their high volume and the fact that
they are continuously augmented means that it takes considerable time to
analyze them. We propose a method for estimating a finite mixture of logistic
regression models which can be used to cluster customers based on a continuous
stream of responses. This method, which we coin oFMLR, allows segments to be
identified in data streams or extremely large static datasets. Contrary to
black box algorithms, oFMLR provides model estimates that are directly
interpretable. We first introduce oFMLR, explaining in passing general topics
such as online estimation and the EM algorithm, making this paper a high level
overview of possible methods of dealing with large data streams in marketing
practice. Next, we discuss model convergence, identifiability, and relations to
alternative, Bayesian, methods; we also identify more general issues that arise
from dealing with continuously augmented data sets. Finally, we introduce the
oFMLR [R] package and evaluate the method by numerical simulation and by
analyzing a large customer clickstream dataset.

negative:
In recent years online shopping has gained momentum and became an important
venue for customers wishing to save time and simplify their shopping process. A
key advantage of shopping online is the ability to read what other customers
are saying about products of interest. In this work, we aim to maintain this
advantage in situations where extreme brevity is needed, for example, when
shopping by voice. We suggest a novel task of extracting a single
representative helpful sentence from a set of reviews for a given product. The
selected sentence should meet two conditions: first, it should be helpful for a
purchase decision and second, the opinion it expresses should be supported by
multiple reviewers. This task is closely related to the task of Multi Document
Summarization in the product reviews domain but differs in its objective and
its level of conciseness. We collect a dataset in English of sentence
helpfulness scores via crowd-sourcing and demonstrate its reliability despite
the inherent subjectivity involved. Next, we describe a complete model that
extracts representative helpful sentences with positive and negative sentiment
towards the product and demonstrate that it outperforms several baselines.

negative:
In a wide variety of applications, including personalization, we want to
measure the difference in outcome due to an intervention and thus have to deal
with counterfactual inference. The feedback from a customer in any of these
situations is only 'bandit feedback' - that is, a partial feedback based on
whether we chose to intervene or not. Typically randomized experiments are
carried out to understand whether an intervention is overall better than no
intervention. Here we present a feature learning algorithm to learn from a
randomized experiment where the intervention in consideration is most effective
and where it is least effective rather than only focusing on the overall
impact, thus adding a context to our learning mechanism and extract more
information. From the randomized experiment, we learn the feature
representations which divide the population into subpopulations where we
observe statistically significant difference in average customer feedback
between those who were subjected to the intervention and those who were not,
with a level of significance l, where l is a configurable parameter in our
model. We use this information to derive the value of the intervention in
consideration for each instance in the population. With experiments, we show
that using this additional learning, in future interventions, the context for
each instance could be leveraged to decide whether to intervene or not.

negative:
Distant and weak supervision allow to obtain large amounts of labeled
training data quickly and cheaply, but these automatic annotations tend to
contain a high amount of errors. A popular technique to overcome the negative
effects of these noisy labels is noise modelling where the underlying noise
process is modelled. In this work, we study the quality of these estimated
noise models from the theoretical side by deriving the expected error of the
noise model. Apart from evaluating the theoretical results on commonly used
synthetic noise, we also publish NoisyNER, a new noisy label dataset from the
NLP domain that was obtained through a realistic distant supervision technique.
It provides seven sets of labels with differing noise patterns to evaluate
different noise levels on the same instances. Parallel, clean labels are
available making it possible to study scenarios where a small amount of
gold-standard data can be leveraged. Our theoretical results and the
corresponding experiments give insights into the factors that influence the
noise model estimation like the noise distribution and the sampling technique.

negative:
Teaching large classes remains a great challenge, primarily because it is
difficult to attend to all the student needs in a timely manner. Automatic text
summarization systems can be leveraged to summarize the student feedback,
submitted immediately after each lecture, but it is left to be discovered what
makes a good summary for student responses. In this work we explore a new
methodology that effectively extracts summary phrases from the student
responses. Each phrase is tagged with the number of students who raise the
issue. The phrases are evaluated along two dimensions: with respect to text
content, they should be informative and well-formed, measured by the ROUGE
metric; additionally, they shall attend to the most pressing student needs,
measured by a newly proposed metric. This work is enabled by a phrase-based
annotation and highlighting scheme, which is new to the summarization task. The
phrase-based framework allows us to summarize the student responses into a set
of bullet points and present to the instructor promptly.

negative:
Topic models are in widespread use in natural language processing and beyond.
Here, we propose a new framework for the evaluation of probabilistic topic
modeling algorithms based on synthetic corpora containing an unambiguously
defined ground truth topic structure. The major innovation of our approach is
the ability to quantify the agreement between the planted and inferred topic
structures by comparing the assigned topic labels at the level of the tokens.
In experiments, our approach yields novel insights about the relative strengths
of topic models as corpus characteristics vary, and the first evidence of an
"undetectable phase" for topic models when the planted structure is weak. We
also establish the practical relevance of the insights gained for synthetic
corpora by predicting the performance of topic modeling algorithms in
classification tasks in real-world corpora.

negative:
Mixed-initiative systems allow users to interactively provide feedback to
potentially improve system performance. Human feedback can correct model errors
and update model parameters to dynamically adapt to changing data.
Additionally, many users desire the ability to have a greater level of control
and fix perceived flaws in systems they rely on. However, how the ability to
provide feedback to autonomous systems influences user trust is a largely
unexplored area of research. Our research investigates how the act of providing
feedback can affect user understanding of an intelligent system and its
accuracy. We present a controlled experiment using a simulated object detection
system with image data to study the effects of interactive feedback collection
on user impressions. The results show that providing human-in-the-loop feedback
lowered both participants' trust in the system and their perception of system
accuracy, regardless of whether the system accuracy improved in response to
their feedback. These results highlight the importance of considering the
effects of allowing end-user feedback on user trust when designing intelligent
systems.

negative:
Home appliance manufacturers strive to obtain feedback from users to improve
their products and services to build a smart home system. To help manufacturers
develop a smart home system, we design a federated learning (FL) system
leveraging the reputation mechanism to assist home appliance manufacturers to
train a machine learning model based on customers' data. Then, manufacturers
can predict customers' requirements and consumption behaviors in the future.
The working flow of the system includes two stages: in the first stage,
customers train the initial model provided by the manufacturer using both the
mobile phone and the mobile edge computing (MEC) server. Customers collect data
from various home appliances using phones, and then they download and train the
initial model with their local data. After deriving local models, customers
sign on their models and send them to the blockchain. In case customers or
manufacturers are malicious, we use the blockchain to replace the centralized
aggregator in the traditional FL system. Since records on the blockchain are
untampered, malicious customers or manufacturers' activities are traceable. In
the second stage, manufacturers select customers or organizations as miners for
calculating the averaged model using received models from customers. By the end
of the crowdsourcing task, one of the miners, who is selected as the temporary
leader, uploads the model to the blockchain. To protect customers' privacy and
improve the test accuracy, we enforce differential privacy on the extracted
features and propose a new normalization technique. We experimentally
demonstrate that our normalization technique outperforms batch normalization
when features are under differential privacy protection. In addition, to
attract more customers to participate in the crowdsourcing FL task, we design
an incentive mechanism to award participants.

negative:
Research has been devoted in the past few years to relevance feedback as an
effective solution to improve performance of information retrieval systems.
Relevance feedback refers to an interactive process that helps to improve the
retrieval performance. In this paper we propose the use of relevance feedback
to improve document image retrieval System (DIRS) performance. This paper
compares a variety of strategies for positive and negative feedback. In
addition, feature subspace is extracted and updated during the feedback process
using a Principal Component Analysis (PCA) technique and based on user's
feedback. That is, in addition to reducing the dimensionality of feature
spaces, a proper subspace for each type of features is obtained in the feedback
process to further improve the retrieval accuracy. Experiments show that using
relevance Feedback in DIR achieves better performance than common DIR.

negative:
As financial services (FS) companies have experienced drastic technology
driven changes, the availability of new data streams provides the opportunity
for more comprehensive customer understanding. We propose Dynamic Customer
Embeddings (DCE), a framework that leverages customers' digital activity and a
wide range of financial context to learn dense representations of customers in
the FS industry. Our method examines customer actions and pageviews within a
mobile or web digital session, the sequencing of the sessions themselves, and
snapshots of common financial features across our organization at the time of
login. We test our customer embeddings using real world data in three
prediction problems: 1) the intent of a customer in their next digital session,
2) the probability of a customer calling the call centers after a session, and
3) the probability of a digital session to be fraudulent. DCE showed
performance lift in all three downstream problems.

negative:
Obtaining large annotated datasets is critical for training successful
machine learning models and it is often a bottleneck in practice. Weak
supervision offers a promising alternative for producing labeled datasets
without ground truth annotations by generating probabilistic labels using
multiple noisy heuristics. This process can scale to large datasets and has
demonstrated state of the art performance in diverse domains such as healthcare
and e-commerce. One practical issue with learning from user-generated
heuristics is that their creation requires creativity, foresight, and domain
expertise from those who hand-craft them, a process which can be tedious and
subjective. We develop the first framework for interactive weak supervision in
which a method proposes heuristics and learns from user feedback given on each
proposed heuristic. Our experiments demonstrate that only a small number of
feedback iterations are needed to train models that achieve highly competitive
test set performance without access to ground truth training labels. We conduct
user studies, which show that users are able to effectively provide feedback on
heuristics and that test set results track the performance of simulated
oracles.

negative:
A promising approach to improve the robustness and exploration in
Reinforcement Learning is collecting human feedback and that way incorporating
prior knowledge of the target environment. It is, however, often too expensive
to obtain enough feedback of good quality. To mitigate the issue, we aim to
rely on a group of multiple experts (and non-experts) with different skill
levels to generate enough feedback. Such feedback can therefore be inconsistent
and infrequent. In this paper, we build upon prior work -- Advise, a Bayesian
approach attempting to maximise the information gained from human feedback --
extending the algorithm to accept feedback from this larger group of humans,
the trainers, while also estimating each trainer's reliability. We show how
aggregating feedback from multiple trainers improves the total feedback's
accuracy and make the collection process easier in two ways. Firstly, this
approach addresses the case of some of the trainers being adversarial.
Secondly, having access to the information about each trainer reliability
provides a second layer of robustness and offers valuable information for
people managing the whole system to improve the overall trust in the system. It
offers an actionable tool for improving the feedback collection process or
modifying the reward function design if needed. We empirically show that our
approach can accurately learn the reliability of each trainer correctly and use
it to maximise the information gained from the multiple trainers' feedback,
even if some of the sources are adversarial.

negative:
Telecommunications operators (telcos) traditional sources of income, voice
and SMS, are shrinking due to customers using over-the-top (OTT) applications
such as WhatsApp or Viber. In this challenging environment it is critical for
telcos to maintain or grow their market share, by providing users with as good
an experience as possible on their network.
  But the task of extracting customer insights from the vast amounts of data
collected by telcos is growing in complexity and scale everey day. How can we
measure and predict the quality of a user's experience on a telco network in
real-time? That is the problem that we address in this paper.
  We present an approach to capture, in (near) real-time, the mobile customer
experience in order to assess which conditions lead the user to place a call to
a telco's customer care center. To this end, we follow a supervised learning
approach for prediction and train our 'Restricted Random Forest' model using,
as a proxy for bad experience, the observed customer transactions in the telco
data feed before the user places a call to a customer care center.
  We evaluate our approach using a rich dataset provided by a major African
telecommunication's company and a novel big data architecture for both the
training and scoring of predictive models. Our empirical study shows our
solution to be effective at predicting user experience by inferring if a
customer will place a call based on his current context.
  These promising results open new possibilities for improved customer service,
which will help telcos to reduce churn rates and improve customer experience,
both factors that directly impact their revenue growth.

negative:
Data analytics software applications have become an integral part of the
decision-making process of analysts. Users of such a software face challenges
due to insufficient product and domain knowledge, and find themselves in need
of help. To alleviate this, we propose a task-aware command recommendation
system, to guide the user on what commands could be executed next. We rely on
topic modeling techniques to incorporate information about user's task into our
models. We also present a help prediction model to detect if a user is in need
of help, in which case the system proactively provides the aforementioned
command recommendations. We leverage the log data of a web-based analytics
software to quantify the superior performance of our neural models, in
comparison to competitive baselines.

negative:
Motivated by online settings where users can provide explicit feedback about
the relevance of products that are sequentially presented to them, we look at
the recommendation process as a problem of dynamically optimizing this
relevance feedback. Such an algorithm optimizes the fine tradeoff between
presenting the products that are most likely to be relevant, and learning the
preferences of the user so that more relevant recommendations can be made in
the future.
  We assume a standard predictive model inspired by collaborative filtering, in
which a user is sampled from a distribution over a set of possible types. For
every product category, each type has an associated relevance feedback that is
assumed to be binary: the category is either relevant or irrelevant. Assuming
that the user stays for each additional recommendation opportunity with
probability $\beta$ independent of the past, the problem is to find a policy
that maximizes the expected number of recommendations that are deemed relevant
in a session.
  We analyze this problem and prove key structural properties of the optimal
policy. Based on these properties, we first present an algorithm that strikes a
balance between recursion and dynamic programming to compute this policy. We
further propose and analyze two heuristic policies: a `farsighted' greedy
policy that attains at least $1-\beta$ factor of the optimal payoff, and a
naive greedy policy that attains at least $\frac{1-\beta}{1+\beta}$ factor of
the optimal payoff in the worst case. Extensive simulations show that these
heuristics are very close to optimal in practice.

negative:
Recently, topic modeling has been widely used to discover the abstract topics
in text corpora. Most of the existing topic models are based on the assumption
of three-layer hierarchical Bayesian structure, i.e. each document is modeled
as a probability distribution over topics, and each topic is a probability
distribution over words. However, the assumption is not optimal. Intuitively,
it's more reasonable to assume that each topic is a probability distribution
over concepts, and then each concept is a probability distribution over words,
i.e. adding a latent concept layer between topic layer and word layer in
traditional three-layer assumption. In this paper, we verify the proposed
assumption by incorporating the new assumption in two representative topic
models, and obtain two novel topic models. Extensive experiments were conducted
among the proposed models and corresponding baselines, and the results show
that the proposed models significantly outperform the baselines in terms of
case study and perplexity, which means the new assumption is more reasonable
than traditional one.

negative:
Existing document filtering systems learn user profiles based on user
relevance feedback on documents. In some cases, users may have prior knowledge
about what features are important. For example, a Spanish speaker may only want
news written in Spanish, and thus a relevant document should contain the
feature "Language: Spanish"; a researcher focusing on HIV knows an article with
the medical subject "Subject: AIDS" is very likely to be relevant to him/her.
  Semi-structured documents with rich metadata are increasingly prevalent on
the Internet. Motivated by the well-adopted faceted search interface in
e-commerce, we study the exploitation of user prior knowledge on faceted
features for semi-structured document filtering. We envision two faceted
feedback mechanisms, and propose a novel user profile learning algorithm that
can incorporate user feedback on features. To evaluate the proposed work, we
use two data sets from the TREC filtering track, and conduct a user study on
Amazon Mechanical Turk. Our experiment results show that user feedback on
faceted features is useful for filtering. The proposed user profile learning
algorithm can effectively learn from user feedback on both documents and
features, and performs better than several existing methods.

negative:
Manually labeled corpora are expensive to create and often not available for
low-resource languages or domains. Automatic labeling approaches are an
alternative way to obtain labeled data in a quicker and cheaper way. However,
these labels often contain more errors which can deteriorate a classifier's
performance when trained on this data. We propose a noise layer that is added
to a neural network architecture. This allows modeling the noise and train on a
combination of clean and noisy data. We show that in a low-resource NER task we
can improve performance by up to 35% by using additional, noisy data and
handling the noise.

negative:
Convincing a customer is always considered as a challenging task in every
business. But when it comes to online business, this task becomes even more
difficult. Online retailers try everything possible to gain the trust of the
customer. One of the solutions is to provide an area for existing users to
leave their comments. This service can effectively develop the trust of the
customer however normally the customer comments about the product in their
native language using Roman script. If there are hundreds of comments this
makes difficulty even for the native customers to make a buying decision. This
research proposes a system which extracts the comments posted in Roman Urdu,
translate them, find their polarity and then gives us the rating of the
product. This rating will help the native and non-native customers to make
buying decision efficiently from the comments posted in Roman Urdu.

negative:
Alternative recommender systems are critical for ecommerce companies. They
guide customers to explore a massive product catalog and assist customers to
find the right products among an overwhelming number of options. However, it is
a non-trivial task to recommend alternative products that fit customer needs.
In this paper, we use both textual product information (e.g. product titles and
descriptions) and customer behavior data to recommend alternative products. Our
results show that the coverage of alternative products is significantly
improved in offline evaluations as well as recall and precision. The final A/B
test shows that our algorithm increases the conversion rate by 12 percent in a
statistically significant way. In order to better capture the semantic meaning
of product information, we build a Siamese Network with Bidirectional LSTM to
learn product embeddings. In order to learn a similarity space that better
matches the preference of real customers, we use co-compared data from
historical customer behavior as labels to train the network. In addition, we
use NMSLIB to accelerate the computationally expensive kNN computation for
millions of products so that the alternative recommendation is able to scale
across the entire catalog of a major ecommerce site.

negative:
Learning with noisy labels is a common challenge in supervised learning.
Existing approaches often require practitioners to specify noise rates, i.e., a
set of parameters controlling the severity of label noises in the problem, and
the specifications are either assumed to be given or estimated using additional
steps. In this work, we introduce a new family of loss functions that we name
as peer loss functions, which enables learning from noisy labels and does not
require a priori specification of the noise rates. Peer loss functions work
within the standard empirical risk minimization (ERM) framework. We show that,
under mild conditions, performing ERM with peer loss functions on the noisy
dataset leads to the optimal or a near-optimal classifier as if performing ERM
over the clean training data, which we do not have access to. We pair our
results with an extensive set of experiments. Peer loss provides a way to
simplify model development when facing potentially noisy training labels, and
can be promoted as a robust candidate loss function in such situations.

negative:
Recently there has been significant activity in developing algorithms with
provable guarantees for topic modeling. In standard topic models, a topic (such
as sports, business, or politics) is viewed as a probability distribution $\vec
a_i$ over words, and a document is generated by first selecting a mixture $\vec
w$ over topics, and then generating words i.i.d. from the associated mixture
$A{\vec w}$. Given a large collection of such documents, the goal is to recover
the topic vectors and then to correctly classify new documents according to
their topic mixture.
  In this work we consider a broad generalization of this framework in which
words are no longer assumed to be drawn i.i.d. and instead a topic is a complex
distribution over sequences of paragraphs. Since one could not hope to even
represent such a distribution in general (even if paragraphs are given using
some natural feature representation), we aim instead to directly learn a
document classifier. That is, we aim to learn a predictor that given a new
document, accurately predicts its topic mixture, without learning the
distributions explicitly. We present several natural conditions under which one
can do this efficiently and discuss issues such as noise tolerance and sample
complexity in this model. More generally, our model can be viewed as a
generalization of the multi-view or co-training setting in machine learning.

negative:
Capturing users' engagement is crucial for gathering feedback about the
features of a software product. In a market-driven context, current approaches
to collect and analyze users' feedback are based on techniques leveraging
information extracted from product reviews and social media. These approaches
are hardly applicable in bespoke software development, or in contexts in which
one needs to gather information from specific users. In such cases, companies
need to resort to face-to-face interviews to get feedback on their products. In
this paper, we propose to utilize biometric data, in terms of physiological and
voice features, to complement interviews with information about the engagement
of the user on the discussed product-relevant topics. We evaluate our approach
by interviewing users while gathering their physiological data (i.e.,
biofeedback) using an Empatica E4 wristband, and capturing their voice through
the default audio-recorder of a common laptop. Our results show that we can
predict users' engagement by training supervised machine learning algorithms on
biometric data (F1=0.72), and that voice features alone are sufficiently
effective (F1=0.71). Our work contributes with one the first studies in
requirements engineering in which biometrics are used to identify emotions.
This is also the first study in software engineering that considers voice
analysis. The usage of voice features could be particularly helpful for
emotion-aware requirements elicitation in remote communication, either
performed by human analysts or voice-based chatbots, and can also be exploited
to support the analysis of meetings in software engineering research.

negative:
We present and study models of adversarial online learning where the feedback
observed by the learner is noisy, and the feedback is either full information
feedback or bandit feedback. Specifically, we consider binary losses xored with
the noise, which is a Bernoulli random variable. We consider both a constant
noise rate and a variable noise rate. Our main results are tight regret bounds
for learning with noise in the adversarial online learning model.

negative:
Users giving relevance feedback in exploratory search are often uncertain
about the correctness of their feedback, which may result in noisy or even
erroneous feedback. Additionally, the search intent of the user may be volatile
as the user is constantly learning and reformulating her search hypotheses
during the search. This may lead to a noticeable concept drift in the feedback.
We formulate a Bayesian regression model for predicting the accuracy of each
individual user feedback and thus find outliers in the feedback data set.
Additionally, we introduce a timeline interface that visualizes the feedback
history to the user and gives her suggestions on which past feedback is likely
in need of adjustment. This interface also allows the user to adjust the
feedback accuracy inferences made by the model. Simulation experiments
demonstrate that the performance of the new user model outperforms a simpler
baseline and that the performance approaches that of an oracle, given a small
amount of additional user interaction. A user study shows that the proposed
modelling technique, combined with the timeline interface, makes it easier for
the users to notice and correct mistakes in their feedback, and to discover new
items.

negative:
While buying a product from the e-commerce websites, customers generally have
a plethora of questions. From the perspective of both the e-commerce service
provider as well as the customers, there must be an effective question
answering system to provide immediate answers to the user queries. While
certain questions can only be answered after using the product, there are many
questions which can be answered from the product specification itself. Our work
takes a first step in this direction by finding out the relevant product
specifications, that can help answering the user questions. We propose an
approach to automatically create a training dataset for this problem. We
utilize recently proposed XLNet and BERT architectures for this problem and
find that they provide much better performance than the Siamese model,
previously applied for this problem. Our model gives a good performance even
when trained on one vertical and tested across different verticals.

negative:
E-commerce websites use machine learned ranking models to serve shopping
results to customers. Typically, the websites log the customer search events,
which include the query entered and the resulting engagement with the shopping
results, such as clicks and purchases. Each customer search event serves as
input training data for the models, and the individual customer engagement
serves as a signal for customer preference. So a purchased shopping result, for
example, is perceived to be more important than one that is not. However, new
or under-impressed products do not have enough customer engagement signals and
end up at a disadvantage when being ranked alongside popular products. In this
paper, we propose a novel method for data curation that aggregates all customer
engagements within a day for the same query to use as input training data. This
aggregated customer engagement gives the models a complete picture of the
relative importance of shopping results. Training models on this aggregated
data leads to less reliance on behavioral features. This helps mitigate the
cold start problem and boosted relevant new products to top search results. In
this paper, we present the offline and online analysis and results comparing
the individual and aggregated customer engagement models trained on e-commerce
data.

negative:
This paper addresses the problem of multiclass classification with corrupted
or noisy bandit feedback. In this setting, the learner may not receive true
feedback. Instead, it receives feedback that has been flipped with some
non-zero probability. We propose a novel approach to deal with noisy bandit
feedback based on the unbiased estimator technique. We further offer a method
that can efficiently estimate the noise rates, thus providing an end-to-end
framework. The proposed algorithm enjoys a mistake bound of the order of
$O(\sqrt{T})$ in the high noise case and of the order of
$O(T^{\nicefrac{2}{3}})$ in the worst case. We show our approach's
effectiveness using extensive experiments on several benchmark datasets.

negative:
Recommendation is the task of improving customer experience through
personalized recommendation based on users' past feedback. In this paper, we
investigate the most common scenario: the user-item (U-I) matrix of implicit
feedback. Even though many recommendation approaches are designed based on
implicit feedback, they attempt to project the U-I matrix into a low-rank
latent space, which is a strict restriction that rarely holds in practice. In
addition, although misclassification costs from imbalanced classes are
significantly different, few methods take the cost of classification error into
account. To address aforementioned issues, we propose a robust framework by
decomposing the U-I matrix into two components: (1) a low-rank matrix that
captures the common preference, and (2) a sparse matrix that detects the
user-specific preference of individuals. A cost-sensitive learning model is
embedded into the framework. Specifically, this model exploits different costs
in the loss function for the observed and unobserved instances. We show that
the resulting non-smooth convex objective can be optimized efficiently by an
accelerated projected gradient method with closed-form solutions. Morever, the
proposed algorithm can be scaled up to large-sized datasets after a relaxation.
The theoretical result shows that even with a small fraction of 1's in the U-I
matrix $M\in\mathbb{R}^{n\times m}$, the cost-sensitive error of the proposed
model is upper bounded by $O(\frac{\alpha}{\sqrt{mn}})$, where $\alpha$ is a
bias over imbalanced classes. Finally, empirical experiments are extensively
carried out to evaluate the effectiveness of our proposed algorithm.
Encouraging experimental results show that our algorithm outperforms several
state-of-the-art algorithms on benchmark recommendation datasets.

negative:
We address online learning in complex auction settings, such as sponsored
search auctions, where the value of the bidder is unknown to her, evolving in
an arbitrary manner and observed only if the bidder wins an allocation. We
leverage the structure of the utility of the bidder and the partial feedback
that bidders typically receive in auctions, in order to provide algorithms with
regret rates against the best fixed bid in hindsight, that are exponentially
faster in convergence in terms of dependence on the action space, than what
would have been derived by applying a generic bandit algorithm and almost
equivalent to what would have been achieved in the full information setting.
Our results are enabled by analyzing a new online learning setting with
outcome-based feedback, which generalizes learning with feedback graphs. We
provide an online learning algorithm for this setting, of independent interest,
with regret that grows only logarithmically with the number of actions and
linearly only in the number of potential outcomes (the latter being very small
in most auction settings). Last but not least, we show that our algorithm
outperforms the bandit approach experimentally and that this performance is
robust to dropping some of our theoretical assumptions or introducing noise in
the feedback that the bidder receives.

negative:
Intelligent assistants change the way people interact with computers and make
it possible for people to search for products through conversations when they
have purchase needs. During the interactions, the system could ask questions on
certain aspects of the ideal products to clarify the users' needs. For example,
previous work proposed to ask users the exact characteristics of their ideal
items before showing results. However, users may not have clear ideas about
what an ideal item looks like, especially when they have not seen any item. So
it is more feasible to facilitate the conversational search by showing example
items and asking for feedback instead. In addition, when the users provide
negative feedback for the presented items, it is easier to collect their
detailed feedback on certain properties (aspect-value pairs) of the
non-relevant items. By breaking down the item-level negative feedback to
fine-grained feedback on aspect-value pairs, more information is available to
help clarify users' intents. So in this paper, we propose a conversational
paradigm for product search driven by non-relevant items, based on which
fine-grained feedback is collected and utilized to show better results in the
next iteration. We then propose an aspect-value likelihood model to incorporate
both positive and negative feedback on fine-grained aspect-value pairs of the
non-relevant items. Experimental results show that our model is significantly
better than state-of-the-art product search baselines without using feedback
and those baselines using item-level negative feedback.

negative:
Product search serves as an important entry point for online shopping. In
contrast to web search, the retrieved results in product search not only need
to be relevant but also should satisfy customers' preferences in order to
elicit purchases. Previous work has shown the efficacy of purchase history in
personalized product search. However, customers with little or no purchase
history do not benefit from personalized product search. Furthermore,
preferences extracted from a customer's purchase history are usually long-term
and may not always align with her short-term interests. Hence, in this paper,
we leverage clicks within a query session, as implicit feedback, to represent
users' hidden intents, which further act as the basis for re-ranking subsequent
result pages for the query. It has been studied extensively to model user
preference with implicit feedback in recommendation tasks. However, there has
been little research on modeling users' short-term interest in product search.
We study whether short-term context could help promote users' ideal item in the
following result pages for a query. Furthermore, we propose an end-to-end
context-aware embedding model which can capture long-term and short-term
context dependencies. Our experimental results on the datasets collected from
the search log of a commercial product search engine show that short-term
context leads to much better performance compared with long-term and no
context. Our results also show that our proposed model is more effective than
word-based context-aware models.

negative:
Non-technical losses (NTL) occur during the distribution of electricity in
power grids and include, but are not limited to, electricity theft and faulty
meters. In emerging countries, they may range up to 40% of the total
electricity distributed. In order to detect NTLs, machine learning methods are
used that learn irregular consumption patterns from customer data and
inspection results. The Big Data paradigm followed in modern machine learning
reflects the desire of deriving better conclusions from simply analyzing more
data, without the necessity of looking at theory and models. However, the
sample of inspected customers may be biased, i.e. it does not represent the
population of all customers. As a consequence, machine learning models trained
on these inspection results are biased as well and therefore lead to unreliable
predictions of whether customers cause NTL or not. In machine learning, this
issue is called covariate shift and has not been addressed in the literature on
NTL detection yet. In this work, we present a novel framework for quantifying
and visualizing covariate shift. We apply it to a commercial data set from
Brazil that consists of 3.6M customers and 820K inspection results. We show
that some features have a stronger covariate shift than others, making
predictions less reliable. In particular, previous inspections were focused on
certain neighborhoods or customer classes and that they were not sufficiently
spread among the population of customers. This framework is about to be
deployed in a commercial product for NTL detection.

negative:
For a successful business, engaging in an effective campaign is a key task
for marketers. Most previous studies used various mathematical models to
segment customers without considering the correlation between customer
segmentation and a campaign. This work presents a conceptual model by studying
the significant campaign-dependent variables of customer targeting in customer
segmentation context. In this way, the processes of customer segmentation and
targeting thus can be linked and solved together. The outcomes of customer
segmentation of this study could be more meaningful and relevant for marketers.
This investigation applies a customer life time value (LTV) model to assess the
fitness between targeted customer groups and marketing strategies. To integrate
customer segmentation and customer targeting, this work uses the genetic
algorithm (GA) to determine the optimized marketing strategy. Later, we suggest
using C&RT (Classification and Regression Tree) in SPSS PASW Modeler as the
replacement to Genetic Algorithm technique to accomplish these results. We also
suggest using LOSSYCOUNTING and Counting Bloom Filter to dynamically design the
right and up-to-date offer to the right customer.

negative:
This research proposes a new (old) metric for evaluating goodness of fit in
topic models, the coefficient of determination, or $R^2$. Within the context of
topic modeling, $R^2$ has the same interpretation that it does when used in a
broader class of statistical models. Reporting $R^2$ with topic models
addresses two current problems in topic modeling: a lack of standard
cross-contextual evaluation metrics for topic modeling and ease of
communication with lay audiences. The author proposes that $R^2$ should be
reported as a standard metric when constructing topic models.

negative:
A key challenge in Imitation Learning (IL) is that optimal state actions
demonstrations are difficult for the teacher to provide. For example in
robotics, providing kinesthetic demonstrations on a robotic manipulator
requires the teacher to control multiple degrees of freedom at once. The
difficulty of requiring optimal state action demonstrations limits the space of
problems where the teacher can provide quality feedback. As an alternative to
state action demonstrations, the teacher can provide corrective feedback such
as their preferences or rewards. Prior work has created algorithms designed to
learn from specific types of noisy feedback, but across teachers and tasks
different forms of feedback may be required. Instead we propose that in order
to learn from a diversity of scenarios we need to learn from a variety of
feedback. To learn from a variety of feedback we make the following insight:
the teacher's cost function is latent and we can model a stream of feedback as
a stream of loss functions. We then use any online learning algorithm to
minimize the sum of these losses. With this insight we can learn from a
diversity of feedback that is weakly correlated with the teacher's true cost
function. We unify prior work into a general corrective feedback meta-algorithm
and show that regardless of feedback we can obtain the same regret bounds. We
demonstrate our approach by learning to perform a household navigation task on
a robotic racecar platform. Our results show that our approach can learn
quickly from a variety of noisy feedback.

negative:
The main idea of this paper is to represent shopping items through vectors
because these vectors act as the base for building em- beddings for customers
and shopping carts. Also, these vectors are input to the mathematical models
that act as either a recommendation engine or help in targeting potential
customers. We have used exponential family embeddings as the tool to construct
two basic vectors - product embeddings and context vectors. Using the basic
vectors, we build combined embeddings, trip embeddings and customer embeddings.
Combined embeddings mix linguistic properties of product names with their
shopping patterns. The customer embeddings establish an understand- ing of the
buying pattern of customers in a group and help in building customer profile.
For example a customer profile can represent customers frequently buying
pet-food. Identifying such profiles can help us bring out offers and discounts.
Similarly, trip embeddings are used to build trip profiles. People happen to
buy similar set of products in a trip and hence their trip embeddings can be
used to predict the next product they would like to buy. This is a novel
technique and the first of its kind to make recommendation using product, trip
and customer embeddings.

negative:
Encoder-decoder based neural architectures serve as the basis of
state-of-the-art approaches in end-to-end open domain dialog systems. Since
most of such systems are trained with a maximum likelihood~(MLE) objective they
suffer from issues such as lack of generalizability and the generic response
problem, i.e., a system response that can be an answer to a large number of
user utterances, e.g., "Maybe, I don't know." Having explicit feedback on the
relevance and interestingness of a system response at each turn can be a useful
signal for mitigating such issues and improving system quality by selecting
responses from different approaches. Towards this goal, we present a system
that evaluates chatbot responses at each dialog turn for coherence and
engagement. Our system provides explicit turn-level dialog quality feedback,
which we show to be highly correlated with human evaluation. To show that
incorporating this feedback in the neural response generation models improves
dialog quality, we present two different and complementary mechanisms to
incorporate explicit feedback into a neural response generation model:
reranking and direct modification of the loss function during training. Our
studies show that a response generation model that incorporates these combined
feedback mechanisms produce more engaging and coherent responses in an
open-domain spoken dialog setting, significantly improving the response quality
using both automatic and human evaluation.

negative:
Realistic free-space optical communications systems suffer from turbulent
propagation of light through the atmosphere and detector noise at the receiver,
which can significantly degrade the optical mode quality of the received state,
increase cross-talk between modes, and correspondingly increase the symbol
error ratio (SER) of the system. In order to overcome these obstacles, we
develop a state-of-the-art generative machine learning (GML) and convolutional
neural network (CNN) system in combination, and demonstrate its efficacy in a
free-space optical (FSO) communications setting. The system corrects for the
distortion effects due to turbulence and reduces detector noise, resulting in
significantly lowered SERs and cross-talk at the output of the receiver, while
requiring no feedback. This scheme is straightforward to scale, and may provide
a concrete and cost effective technique to establishing long range classical
and quantum communication links in the near future.

negative:
In black-box optimization problems, we aim to maximize an unknown objective
function, where the function is only accessible through feedbacks of an
evaluation or simulation oracle. In real-life, the feedbacks of such oracles
are often noisy and available after some unknown delay that may depend on the
computation time of the oracle. Additionally, if the exact evaluations are
expensive but coarse approximations are available at a lower cost, the
feedbacks can have multi-fidelity. In order to address this problem, we propose
a generic extension of hierarchical optimistic tree search (HOO), called
ProCrastinated Tree Search (PCTS), that flexibly accommodates a delay and
noise-tolerant bandit algorithm. We provide a generic proof technique to
quantify regret of PCTS under delayed, noisy, and multi-fidelity feedbacks.
Specifically, we derive regret bounds of PCTS enabled with delayed-UCB1 (DUCB1)
and delayed-UCB-V (DUCBV) algorithms. Given a horizon $T$, PCTS retains the
regret bound of non-delayed HOO for expected delay of $O(\log T)$ and worsens
by $O(T^{\frac{1-\alpha}{d+2}})$ for expected delays of $O(T^{1-\alpha})$ for
$\alpha \in (0,1]$. We experimentally validate on multiple synthetic functions
and hyperparameter tuning problems that PCTS outperforms the state-of-the-art
black-box optimization methods for feedbacks with different noise levels,
delays, and fidelity.

negative:
Modeling and synthesizing image noise is an important aspect in many computer
vision applications. The long-standing additive white Gaussian and
heteroscedastic (signal-dependent) noise models widely used in the literature
provide only a coarse approximation of real sensor noise. This paper introduces
Noise Flow, a powerful and accurate noise model based on recent normalizing
flow architectures. Noise Flow combines well-established basic parametric noise
models (e.g., signal-dependent noise) with the flexibility and expressiveness
of normalizing flow networks. The result is a single, comprehensive, compact
noise model containing fewer than 2500 parameters yet able to represent
multiple cameras and gain factors. Noise Flow dramatically outperforms existing
noise models, with 0.42 nats/pixel improvement over the camera-calibrated noise
level functions, which translates to 52% improvement in the likelihood of
sampled noise. Noise Flow represents the first serious attempt to go beyond
simple parametric models to one that leverages the power of deep learning and
data-driven noise distributions.

negative:
Human feedback is widely used to train agents in many domains. However,
previous works rarely consider the uncertainty when humans provide feedback,
especially in cases that the optimal actions are not obvious to the trainers.
For example, the reward of a sub-optimal action can be stochastic and sometimes
exceeds that of the optimal action, which is common in games or real-world.
Trainers are likely to provide positive feedback to sub-optimal actions,
negative feedback to the optimal actions and even do not provide feedback in
some confusing situations. Existing works, which utilize the Expectation
Maximization (EM) algorithm and treat the feedback model as hidden parameters,
do not consider uncertainties in the learning environment and human feedback.
To address this challenge, we introduce a novel feedback model that considers
the uncertainty of human feedback. However, this incurs intractable calculus in
the EM algorithm. To this end, we propose a novel approximate EM algorithm, in
which we approximate the expectation step with the Gradient Descent method.
Experimental results in both synthetic scenarios and two real-world scenarios
with human participants demonstrate the superior performance of our proposed
approach.

negative:
Contextual dynamic pricing aims to set personalized prices based on
sequential interactions with customers. At each time period, a customer who is
interested in purchasing a product comes to the platform. The customer's
valuation for the product is a linear function of contexts, including product
and customer features, plus some random market noise. The seller does not
observe the customer's true valuation, but instead needs to learn the valuation
by leveraging contextual information and historical binary purchase feedbacks.
Existing models typically assume full or partial knowledge of the random noise
distribution. In this paper, we consider contextual dynamic pricing with
unknown random noise in the valuation model. Our distribution-free pricing
policy learns both the contextual function and the market noise simultaneously.
A key ingredient of our method is a novel perturbed linear bandit framework,
where a modified linear upper confidence bound algorithm is proposed to balance
the exploration of market noise and the exploitation of the current knowledge
for better pricing. We establish the regret upper bound and a matching lower
bound of our policy in the perturbed linear bandit framework and prove a
sub-linear regret bound in the considered pricing problem. Finally, we
demonstrate the superior performance of our policy on simulations and a
real-life auto-loan dataset.

negative:
Query categorization is an essential part of query intent understanding in
e-commerce search. A common query categorization task is to select the relevant
fine-grained product categories in a product taxonomy. For frequent queries,
rich customer behavior (e.g., click-through data) can be used to infer the
relevant product categories. However, for more rare queries, which cover a
large volume of search traffic, relying solely on customer behavior may not
suffice due to the lack of this signal. To improve categorization of rare
queries, we adapt the Pseudo-Relevance Feedback (PRF) approach to utilize the
latent knowledge embedded in semantically or lexically similar product
documents to enrich the representation of the more rare queries. To this end,
we propose a novel deep neural model named Attentive Pseudo Relevance Feedback
Network (APRF-Net) to enhance the representation of rare queries for query
categorization. To demonstrate the effectiveness of our approach, we collect
search queries from a large commercial search engine, and compare APRF-Net to
state-of-the-art deep learning models for text classification. Our results show
that the APRF-Net significantly improves query categorization by 5.9% on F1@1
score over the baselines, which increases to 8.2% improvement for the rare
(tail) queries. The findings of this paper can be leveraged for further
improvements in search query representation and understanding.

negative:
Recommendation systems are often evaluated based on user's interactions that
were collected from an existing, already deployed recommendation system. In
this situation, users only provide feedback on the exposed items and they may
not leave feedback on other items since they have not been exposed to them by
the deployed system. As a result, the collected feedback dataset that is used
to evaluate a new model is influenced by the deployed system, as a form of
closed loop feedback. In this paper, we show that the typical offline
evaluation of recommender systems suffers from the so-called Simpson's paradox.
Simpson's paradox is the name given to a phenomenon observed when a significant
trend appears in several different sub-populations of observational data but
disappears or is even reversed when these sub-populations are combined
together. Our in-depth experiments based on stratified sampling reveal that a
very small minority of items that are frequently exposed by the deployed system
plays a confounding factor in the offline evaluation of recommendation systems.
In addition, we propose a novel evaluation methodology that takes into account
the confounder, i.e the deployed system's characteristics. Using the relative
comparison of many recommendation models as in the typical offline evaluation
of recommender systems, and based on the Kendall rank correlation coefficient,
we show that our proposed evaluation methodology exhibits statistically
significant improvements of 14% and 40% on the examined open loop datasets
(Yahoo! and Coat), respectively, in reflecting the true ranking of systems with
an open loop (randomised) evaluation in comparison to the standard evaluation.

negative:
One of the goals of every business enterprise is to increase customer
loyalty. The degree of customer loyalty is called customer quality which its
forecasting will affect strategic marketing practices. The purpose of this
study is to predict the quality of customers of large e-commerce social
networks by big data algorithms and unsupervised learning. For this purpose, a
graph-based social network analysis framework was used for community detection
in the Stanford Network Analysis Platform (SNAP). Then in the found
communities, the quality of customers was predicted. The results showed that
various visits with an impact of 37.13% can have the greatest impact on
customer quality and the order of impact of other parameters were from highest
to lowest: number of frequent customer visits (28.56%), role in social networks
(28.37%), Indirect transactions (26.74%), activity days (25.62%) and customer
social network size (25.06%).

negative:
We propose a general framework for interactively learning models, such as
(binary or non-binary) classifiers, orderings/rankings of items, or clusterings
of data points. Our framework is based on a generalization of Angluin's
equivalence query model and Littlestone's online learning model: in each
iteration, the algorithm proposes a model, and the user either accepts it or
reveals a specific mistake in the proposal. The feedback is correct only with
probability $p > 1/2$ (and adversarially incorrect with probability $1 - p$),
i.e., the algorithm must be able to learn in the presence of arbitrary noise.
The algorithm's goal is to learn the ground truth model using few iterations.
  Our general framework is based on a graph representation of the models and
user feedback. To be able to learn efficiently, it is sufficient that there be
a graph $G$ whose nodes are the models and (weighted) edges capture the user
feedback, with the property that if $s, s^*$ are the proposed and target
models, respectively, then any (correct) user feedback $s'$ must lie on a
shortest $s$-$s^*$ path in $G$. Under this one assumption, there is a natural
algorithm reminiscent of the Multiplicative Weights Update algorithm, which
will efficiently learn $s^*$ even in the presence of noise in the user's
feedback.
  From this general result, we rederive with barely any extra effort classic
results on learning of classifiers and a recent result on interactive
clustering; in addition, we easily obtain new interactive learning algorithms
for ordering/ranking.

negative:
In supervised learning, we typically leverage a fully labeled dataset to
design methods for function estimation or prediction. In many practical
situations, we are able to obtain alternative feedback, possibly at a low cost.
A broad goal is to understand the usefulness of, and to design algorithms to
exploit, this alternative feedback. In this paper, we consider a
semi-supervised regression setting, where we obtain additional ordinal (or
comparison) information for the unlabeled samples. We consider ordinal feedback
of varying qualities where we have either a perfect ordering of the samples, a
noisy ordering of the samples or noisy pairwise comparisons between the
samples. We provide a precise quantification of the usefulness of these types
of ordinal feedback in both nonparametric and linear regression, showing that
in many cases it is possible to accurately estimate an underlying function with
a very small labeled set, effectively \emph{escaping the curse of
dimensionality}. We also present lower bounds, that establish fundamental
limits for the task and show that our algorithms are optimal in a variety of
settings. Finally, we present extensive experiments on new datasets that
demonstrate the efficacy and practicality of our algorithms and investigate
their robustness to various sources of noise and model misspecification.

negative:
We study the interplay between feedback and communication in a cooperative
online learning setting where a network of agents solves a task in which the
learners' feedback is determined by an arbitrary graph. We characterize regret
in terms of the independence number of the strong product between the feedback
graph and the communication network. Our analysis recovers as special cases
many previously known bounds for distributed online learning with either expert
or bandit feedback. A more detailed version of our results also captures the
dependence of the regret on the delay caused by the time the information takes
to traverse each graph. Experiments run on synthetic data show that the
empirical behavior of our algorithm is consistent with the theoretical results.

negative:
We study a general problem of allocating limited resources to heterogeneous
customers over time under model uncertainty. Each type of customer can be
serviced using different actions, each of which stochastically consumes some
combination of resources, and returns different rewards for the resources
consumed. We consider a general model where the resource consumption
distribution associated with each (customer type, action)-combination is not
known, but is consistent and can be learned over time. In addition, the
sequence of customer types to arrive over time is arbitrary and completely
unknown.
  We overcome both the challenges of model uncertainty and customer
heterogeneity by judiciously synthesizing two algorithmic frameworks from the
literature: inventory balancing, which "reserves" a portion of each resource
for high-reward customer types which could later arrive, and online learning,
which shows how to "explore" the resource consumption distributions of each
customer type under different actions. We define an auxiliary problem, which
allows for existing competitive ratio and regret bounds to be seamlessly
integrated. Furthermore, we show that the performance guarantee generated by
our framework is tight, that is, we provide an information-theoretic lower
bound which shows that both the loss from competitive ratio and the loss for
regret are relevant in the combined problem.
  Finally, we demonstrate the efficacy of our algorithms on a publicly
available hotel data set. Our framework is highly practical in that it requires
no historical data (no fitted customer choice models, nor forecasting of
customer arrival patterns) and can be used to initialize allocation strategies
in fast-changing environments.

negative:
With high forward gain, a negative feedback system has the ability to perform
the inverse of a linear or non linear function that is in the feedback path.
This property of negative feedback systems has been widely used in analog
circuits to construct precise closed-loop functions. This paper describes how
the property of a negative feedback system to perform inverse of a function can
be used for training neural networks. This method does not require that the
cost or activation functions be differentiable. Hence, it is able to learn a
class of non-differentiable functions as well where a gradient descent-based
method fails. We also show that gradient descent emerges as a special case of
the proposed method. We have applied this method to the MNIST dataset and
obtained results that shows the method is viable for neural network training.
This method, to the best of our knowledge, is novel in machine learning.

negative:
This work focuses on dynamic regret of online convex optimization that
compares the performance of online learning to a clairvoyant who knows the
sequence of loss functions in advance and hence selects the minimizer of the
loss function at each step. By assuming that the clairvoyant moves slowly
(i.e., the minimizers change slowly), we present several improved
variation-based upper bounds of the dynamic regret under the true and noisy
gradient feedback, which are {\it optimal} in light of the presented lower
bounds. The key to our analysis is to explore a regularity metric that measures
the temporal changes in the clairvoyant's minimizers, to which we refer as {\it
path variation}. Firstly, we present a general lower bound in terms of the path
variation, and then show that under full information or gradient feedback we
are able to achieve an optimal dynamic regret. Secondly, we present a lower
bound with noisy gradient feedback and then show that we can achieve optimal
dynamic regrets under a stochastic gradient feedback and two-point bandit
feedback. Moreover, for a sequence of smooth loss functions that admit a small
variation in the gradients, our dynamic regret under the two-point bandit
feedback matches what is achieved with full information.

negative:
In e-commerce markets, on time delivery is of great importance to customer
satisfaction. In this paper, we present a Deep Reinforcement Learning (DRL)
approach for deciding how and when orders should be batched and picked in a
warehouse to minimize the number of tardy orders. In particular, the technique
facilitates making decisions on whether an order should be picked individually
(pick-by-order) or picked in a batch with other orders (pick-by-batch), and if
so with which other orders. We approach the problem by formulating it as a
semi-Markov decision process and develop a vector-based state representation
that includes the characteristics of the warehouse system. This allows us to
create a deep reinforcement learning solution that learns a strategy by
interacting with the environment and solve the problem with a proximal policy
optimization algorithm. We evaluate the performance of the proposed DRL
approach by comparing it with several batching and sequencing heuristics in
different problem settings. The results show that the DRL approach is able to
develop a strategy that produces consistent, good solutions and performs better
than the proposed heuristics.

negative:
We study episodic reinforcement learning in Markov decision processes when
the agent receives additional feedback per step in the form of several
transition observations. Such additional observations are available in a range
of tasks through extended sensors or prior knowledge about the environment
(e.g., when certain actions yield similar outcome). We formalize this setting
using a feedback graph over state-action pairs and show that model-based
algorithms can leverage the additional feedback for more sample-efficient
learning. We give a regret bound that, ignoring logarithmic factors and
lower-order terms, depends only on the size of the maximum acyclic subgraph of
the feedback graph, in contrast with a polynomial dependency on the number of
states and actions in the absence of a feedback graph. Finally, we highlight
challenges when leveraging a small dominating set of the feedback graph as
compared to the bandit setting and propose a new algorithm that can use
knowledge of such a dominating set for more sample-efficient learning of a
near-optimal policy.

negative:
Relevance Feedback in Content-Based Image Retrieval is a method where the
feedback of the performance is being used to improve itself. Prior works use
feature re-weighting and classification techniques as the Relevance Feedback
methods. This paper shows a novel addition to the prior methods to further
improve the retrieval accuracy. In addition to all of these, the paper also
shows a novel idea to even improve the 0-th iteration retrieval accuracy from
the information of Relevance Feedback.

negative:
We study a pricing setting where each customer is offered a contextualized
price based on customer and/or product features. Often only historical sales
data are available, so we observe whether a customer purchased a product at the
price prescribed rather than the customer's true valuation. Such observational
data are influenced by historical pricing policies, which introduce
difficulties in evaluating the effectiveness of future policies. The goal of
this paper is to formulate loss functions that can be used for evaluating
pricing policies directly from observational data, rather than going through an
intermediate demand estimation stage, which may suffer from bias. To achieve
this, we adapt ideas from machine learning with corrupted labels, where we
consider each observed purchase decision as a known probabilistic
transformation of the customer's valuation. From this transformation, we derive
a class of unbiased loss functions. Within this class, we identify minimum
variance estimators and estimators robust to poor demand estimation.
Furthermore, we show that for contextual pricing, estimators popular in the
off-policy evaluation literature fall within this class of loss functions. We
offer managerial insights into scenarios under which these estimators are
effective.

negative:
To identify the most appropriate recommendation model for an e-commerce
business, a live evaluation should be performed on the shopping website to
measure the influence of personalization in real-time. The aim of this paper is
to introduce and justify two new metrics -- CTR NoRepeat and Click & Buy rate
-- which stem from the standard metrics, Click-through(CTR) and Buy-through
rate(BTR), respectively. The former variation tackles the issue of
overestimation of clicks in the original CTR while the latter accounts for
noting purchases of products that have been previously clicked, in order to
validate that the buy included in the metric is a result of customer
interactions. A significance test for independence of two means is conducted
for multiple datasets, between each of the new metrics and its respective
parent to determine the novelty and necessity of the variants. The
Pearson-correlation coefficient is calculated to assess the strength of the
linear relationships and conclude on the predictability factor amongst the
aforementioned factors to investigate unknown connections between customer
clicks and buys. Additionally, other metrics such as hits per customer, buyers
per customer, clicks per customer etc. are introduced that help explain
indicators of customer behavior on the e-commerce website in reference.

negative:
Through the method of Learning Feedback Linearization, we seek to learn a
linearizing controller to simplify the process of controlling a car to race
autonomously. A soft actor-critic approach is used to learn a decoupling matrix
and drift vector that effectively correct for errors in a hand-designed
linearizing controller. The result is an exactly linearizing controller that
can be used to enable the well-developed theory of linear systems to design
path planning and tracking schemes that are easy to implement and significantly
less computationally demanding. To demonstrate the method of feedback
linearization, it is first used to learn a simulated model whose exact
structure is known, but varied from the initial controller, so as to introduce
error. We further seek to apply this method to a system that introduces even
more error in the form of a gym environment specifically designed for modeling
the dynamics of car racing. To do so, we posit an extension to the method of
learning feedback linearization; a neural network that is trained using
supervised learning to convert the output of our linearizing controller to the
required input for the racing environment. Our progress towards these goals is
reported and the next steps in their accomplishment are discussed.

negative:
Personalized size and fit recommendations bear crucial significance for any
fashion e-commerce platform. Predicting the correct fit drives customer
satisfaction and benefits the business by reducing costs incurred due to
size-related returns. Traditional collaborative filtering algorithms seek to
model customer preferences based on their previous orders. A typical challenge
for such methods stems from extreme sparsity of customer-article orders. To
alleviate this problem, we propose a deep learning based content-collaborative
methodology for personalized size and fit recommendation. Our proposed method
can ingest arbitrary customer and article data and can model multiple
individuals or intents behind a single account. The method optimizes a global
set of parameters to learn population-level abstractions of size and fit
relevant information from observed customer-article interactions. It further
employs customer and article specific embedding variables to learn their
properties. Together with learned entity embeddings, the method maps additional
customer and article attributes into a latent space to derive personalized
recommendations. Application of our method to two publicly available datasets
demonstrate an improvement over the state-of-the-art published results. On two
proprietary datasets, one containing fit feedback from fashion experts and the
other involving customer purchases, we further outperform comparable
methodologies, including a recent Bayesian approach for size recommendation.

negative:
In this paper, we relate the feedback capacity of parallel additive colored
Gaussian noise (ACGN) channels to a variant of the Kalman filter. By doing so,
we obtain lower bounds on the feedback capacity of such channels, as well as
the corresponding feedback (recursive) coding schemes, which are essentially
power allocation policies with feedback, to achieve the bounds. The results are
seen to reduce to existing lower bounds in the case of a single ACGN feedback
channel, whereas when it comes to parallel additive white Gaussian noise (AWGN)
channels with feedback, the recursive coding scheme reduces to a feedback
"water-filling" power allocation policy.