Feat2Vec: Dense Vector Representation for Data with Arbitrary Features
Methods that calculate dense vector representations for features in unstructured data—such as words in a document—have proven to be very successful for knowledge representation. We study how to estimate dense representations when multiple feature types exist within a dataset for supervised learning where explicit labels are available, as well as for unsupervised learning where there are no labels. Feat2Vec calculates embeddings for data with multiple feature types enforcing that all different feature types exist in a common space. In the supervised case, we show that our method has advantages over recently proposed methods; such as enabling higher prediction accuracy, and providing a way to avoid the cold-start problem. In the unsupervised case, our experiments suggest that Feat2Vec significantly outperforms existing algorithms that do not leverage the structure of the data. We believe that we are the first to propose a method for learning unsuper vised embeddings that leverage the structure of multiple feature types.
 This paper provides a clean way of learning embeddings for structured features that can be discrete -- indicating presence / absence of a certain quality. Further, these features can be structured i.e. a set of them are of the same *type*. Unlike, word2vec there is no hard constraint that similar objects must have similar representations and so, the learnt embeddings reflect the likelihood of the observed features. Therefore, this can be used as a multi-label classifier by using two feature types -- the input and the set of categories. This proposed scheme is evaluated on two datasets -- movies and education in a retrieval setting. I would like to see an evaluation of these features in a classification setting to further demonstrate the utility of these embeddings as compared to directly embedding the discrete features and then performing a K-way classification. For example, I am aware of -- http://manikvarma.org/downloads/XC/XMLRepository.html contains some interesting datasets which have a large number of discrete features and classes.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Feat2Vec: Dense Vector Representation for Data with Arbitrary Features
Methods that calculate dense vector representations for features in unstructured data—such as words in a document—have proven to be very successful for knowledge representation. We study how to estimate dense representations when multiple feature types exist within a dataset for supervised learning where explicit labels are available, as well as for unsupervised learning where there are no labels. Feat2Vec calculates embeddings for data with multiple feature types enforcing that all different feature types exist in a common space. In the supervised case, we show that our method has advantages over recently proposed methods; such as enabling higher prediction accuracy, and providing a way to avoid the cold-start problem. In the unsupervised case, our experiments suggest that Feat2Vec significantly outperforms existing algorithms that do not leverage the structure of the data. We believe that we are the first to propose a method for learning unsuper vised embeddings that leverage the structure of multiple feature types.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



 This paper provides a clean way of learning embeddings for structured features that can be discrete -- indicating presence / absence of a certain quality. Further, these features can be structured i.e. a set of them are of the same *type*. Unlike, word2vec there is no hard constraint that similar objects must have similar representations and so, the learnt embeddings reflect the likelihood of the observed features. Therefore, this can be used as a multi-label classifier by using two feature types -- the input and the set of categories. This proposed scheme is evaluated on two datasets -- movies and education in a retrieval setting. I would like to see an evaluation of these features in a classification setting to further demonstrate the utility of these embeddings as compared to directly embedding the discrete features and then performing a K-way classification. For example, I am aware of -- http://manikvarma.org/downloads/XC/XMLRepository.html contains some interesting datasets which have a large number of discrete features and classes.
assistant
 This paper provides a clean way of learning embeddings for structured features that can be discrete -- indicating presence / absence of a certain quality. Further, these features can be structured i.e. a set of them are of the same *type*. Unlike, word2vec there is no hard constraint that similar objects must have similar representations and so, the learnt embeddings reflect the likelihood of the observed features. Therefore, this can be used as a multi-label classifier by using two feature types -- the input and the set of categories. This proposed scheme is evaluated on two datasets -- movies and education in a retrieval setting. I would like to see an evaluation of these features in a classification setting to further demonstrate the utility of these embeddings as compared to directly embedding the discrete features and then performing a K-way classification. For example, I am aware of -- http://manikvarma.org/downloads/XC/XMLRepository.html contains some interesting datasets which have a large number of discrete features and classes.