Papers and works on Recommendation System(RecSys) you must know
| Titile | Booktitle | Authors | Resources |
|---|---|---|---|
| Deep Learning Based Recommender System: A Survey and New Perspectives | ACM Computing Surveys (CSUR)'2019 | Shuai Zhang; Lina Yao; Aixin Sun; Yi Tay | [pdf] |
| Sequential Recommender Systems: Challenges, Progress and Prospects | IJCAI'2019 | Shoujin Wang; Liang Hu; Yan Wang; Longbing Cao; Quan Z. Sheng; Mehmet Orgun | [pdf] |
| Real-time Personalization using Embeddings for Search Ranking at Airbnb | KDD'2018 | Mihajlo Grbovic (Airbnb); Haibin Cheng (Airbnb) | [pdf] |
| Deep Neural Networks for YouTube Recommendations | RecSys '2016 | Paul Covington(Google);Jay Adams(Google);Emre Sargin(Google) | [pdf] |
| The Netflix Recommender System: Algorithms, Business Value, and Innovation | ACM TMIS'2015 | Carlos A. Gomez-Uribe(Netflix);Neil Hunt(Netflix) | [pdf] |
| Titile | Booktitle | Resources |
|---|---|---|
| FM: Factorization Machines | ICDM'2010 | [pdf] [code] [tffm] [fmpytorch] |
| libFM: Factorization Machines with libFM | ACM Trans'2012 | [pdf] [code] |
| GBDT+LR: Practical Lessons from Predicting Clicks on Ads at Facebook | ADKDD'14 | [pdf] |
| FFM: Field-aware Factorization Machines for CTR Prediction | RecSys'2016 | [pdf] [code] |
| FNN: Deep Learning over Multi-field Categorical Data: A Case Study on User Response Prediction | ECIR'2016 | [pdf][Tensorflow] |
| PNN: Product-based Neural Networks for User Response Prediction | ICDM'2016 | [pdf][Tensorflow] |
| Wide&Deep: Wide & Deep Learning for Recommender Systems | DLRS'2016 | [pdf][Tensorflow][Blog] |
| AFM: Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks | IJCAI'2017 | [pdf][Tensorflow] |
| NFM: Neural Factorization Machines for Sparse Predictive Analytics | SIGIR'2017 | [pdf][Tensorflow] |
| DeepFM: DeepFM: A Factorization-Machine based Neural Network for CTR Prediction[C] | IJCAI'2017 | [pdf] [code] |
| DCN: Deep & Cross Network for Ad Click Predictions | ADKDD'2017 | [pdf] [Keras][Tensorflow] |
| xDeepFM: xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems | KDD'2018 | [pdf] [Tensorflow] |
| DIN: DIN: Deep Interest Network for Click-Through Rate Prediction | KDD'2018 | [pdf] [Tensorflow] |
| DIEN: DIEN: Deep Interest Evolution Network for Click-Through Rate Prediction | AAAI'2019 | [pdf] [Tensorflow] |
| DSIN: Deep Session Interest Network for Click-Through Rate Prediction | IJCAI'2019 | [pdf][Tensorflow] |
| AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks | CIKM'2019 | [pdf][Tensorflow] |
| FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction | RecSys '19 | [pdf][Tensorflow] |
| DeepGBM:A Deep Learning Framework Distilled by GBDT for Online Prediction Tasks | KDD'2019 | [pdf][Tensorflow] |
| FLEN: Leveraging Field for Scalable CTR Prediction | AAAI'2020 | [pdf][Tensorflow] |
| DFN: Deep Feedback Network for Recommendation | IJCAI'2020 | [pdf][Tensorflow] |
| Titile | Booktitle | Resources |
|---|---|---|
| GRU4Rec:Session-based Recommendations with Recurrent Neural Networks | ICLR'2016 | [pdf][code] |
| DREAM:A Dynamic Recurrent Model for Next Basket Recommendation | SIGIR'2016 | [pdf][code] |
| Long and Short-Term Recommendations with Recurrent Neural Networks | UMAP’2017 | [pdf][Theano] |
| Time-LSTM:What to Do Next: Modeling User Behaviors by Time-LSTM | IJCAI'2017 | [pdf] [code] |
| Caser:Personalized Top-N Sequential Recommendation via Convolutional Sequence EmbeddingCaser | WSDM'2018 | [pdf] [code] |
| SASRec:Self-Attentive Sequential Recommendation | ICDM'2018 | [pdf][code] |
| BERT4Rec:BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer | ACM WOODSTOCK’2019 | [pdf][code] |
| SR-GNN: Session-based Recommendation with Graph Neural Networks | AAAI'2019 | [pdf] [code] |
| Titile | Booktitle | Resources |
|---|---|---|
| RippleNet: RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systems | CIKM'2018 | [pdf] [code] |
| Titile | Booktitle | Resources |
|---|---|---|
| UBCF:GroupLens: an open architecture for collaborative filtering of netnews | CSCW'1994 | [pdf][code] |
| IBCF:Item-based collaborative filtering recommendation algorithms | WWW'2001 | [pdf][code] |
| SVD:Matrix Factorization Techniques for Recommender Systems | Journal Computer'2009 | [pdf][code] |
| SVD++:Factorization meets the neighborhood: a multifaceted collaborative filtering model | KDD'2008 | [pdf][code] |
| PMF: Probabilistic Matrix Factorization | NIPS'2007 | [pdf] [code] |
| CDL:Collaborative Deep Learning for Recommender Systems | KDD '2015 | [pdf][code][PPT] |
| ConvMF:Convolutional Matrix Factorization for Document Context-Aware Recommendation | RecSys'2016 | [pdf][code][zhihu][PPT] |
| NCF:Neural Collaborative Filtering | WWW '17 | pdfcode |
DropoutNet: Addressing Cold Start in Recommender Systems. [pdf] [code]
- KASANDR:KASANDR: A Large-Scale Dataset with Implicit Feedback for Recommendation (SIGIR 2017). [pdf] [KASANDR Data Set ]
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Recommender Systems Specialization Coursera
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Deep Learning for Recommender Systems by Balázs Hidasi. RecSys Summer School, 21-25 August, 2017, Bozen-Bolzano. Slides
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Deep Learning for Recommender Systems by Alexandros Karatzoglou and Balázs Hidasi. RecSys2017 Tutorial. Slides
- Skill Tree pdf
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