
# `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.



{"language": ["en"], "license": "mit", "tags": ["recsys", "pytorch", "sentence_transformers"]}
recsys pytorch sentence_transformers en arxiv:2109.03955 arxiv:1908.10084 license:mit region:us
TAGS
#recsys #pytorch #sentence_transformers #en #arxiv-2109.03955 #arxiv-1908.10084 #license-mit #region-us 


# '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.

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, in particular: 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 accessed on 2022-02-04. In particular to the ones related to Machine Learning and AI:

1. Artificial Intelligence
1. Computation and Language
1. Computer Vision and Pattern Recognition
1. Information Retrieval
1. Machine Learning (cs)
1. Machine Learning (stat)

### 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.



# '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.

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, in particular: 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 accessed on 2022-02-04. In particular to the ones related to Machine Learning and AI:

1. Artificial Intelligence
1. Computation and Language
1. Computer Vision and Pattern Recognition
1. Information Retrieval
1. Machine Learning (cs)
1. Machine Learning (stat) ### 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.
TAGS
#recsys #pytorch #sentence_transformers #en #arxiv-2109.03955 #arxiv-1908.10084 #license-mit #region-us 
 # '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.

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, in particular: 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 accessed on 2022-02-04. In particular to the ones related to Machine Learning and AI:

1. Artificial Intelligence
1. Computation and Language
1. Computer Vision and Pattern Recognition
1. Information Retrieval
1. Machine Learning (cs)
1. Machine Learning (stat) ### 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.
passage: TAGS
#recsys #pytorch #sentence_transformers #en #arxiv-2109.03955 #arxiv-1908.10084 #license-mit #region-us 
# '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.

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, in particular: 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 accessed on 2022-02-04. In particular to the ones related to Machine Learning and AI:

1. Artificial Intelligence
1. Computation and Language
1. Computer Vision and Pattern Recognition
1. Information Retrieval
1. Machine Learning (cs)
1. Machine Learning (stat)### 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.