readme: ---
  language:
  - en
  license: mit
  tags:
  - recsys
  - pytorch
  - sentence_transformers
  
  #datasets:
  #- {dataset_0}  # Example: common_voice. Use dataset id from https://hf.co/datasets
  #metrics:
  #- {metric_0}  # Example: wer. Use metric id from https://hf.co/metrics
  ---
  
  # `paper-rec` Model Card
  
  Last updated: 2022-02-04
  
  ## Model Details
  `paper-rec` goal is to recommend users what scientific papers to read next based on their preferences. This is a test model used to explore Hugging Face Hub capabilities and identify requirements to enable support for recommendation task in the ecosystem. 
  
  ### Model date
  2022-02-04
  
  ### Model type
  Recommender System model with support of a Language Model for feature extraction.
  
  ### Paper & samples
  The overall idea for `paper-rec` test model is inspired by this work: [NU:BRIEF – A Privacy-aware Newsletter Personalization Engine for Publishers](https://arxiv.org/abs/2109.03955).
  
  However, for `paper-rec`, we use a different language model more suitable for longer text, namely *Sentence Transformers*: [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://arxiv.org/abs/1908.10084), in particular: [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2).
  
  ## Model Use
  The intended direct users are recommender systems' practitioners and enthusiasts that would like to experiment with the task of scientific paper recommendation.
  
  ## Data, Performance, and Limitations
  ### Data 
  The data used for this model corresponds to the [RSS news feeds for arXiv updates](https://arxiv.org/help/rss) accessed on 2022-02-04. In particular to the ones related to Machine Learning and AI:
  
  1. [Artificial Intelligence](http://arxiv.org/rss/cs.AI)
  1. [Computation and Language](http://arxiv.org/rss/cs.CL)
  1. [Computer Vision and Pattern Recognition](http://arxiv.org/rss/cs.CV)
  1. [Information Retrieval](http://arxiv.org/rss/cs.IR)
  1. [Machine Learning (cs)](http://arxiv.org/rss/cs.LG)
  1. [Machine Learning (stat)](http://arxiv.org/rss/stat.ML)
  
  ### Performance 
  N/A
  
  ## Limitations
  The model is limited to the papers fetched on 2022-02-04, that is, those papers are the only ones it can recommend.
  
  
  