Awesome
Awesome-RecSys-Works
Papers and works on Recommendation System(RecSys) you must know
Survey Review
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] |
MOBIUS: Towards the Next Generation of Query-Ad Matching in Baidu’s Sponsored Search | KDD ’19 | Baidu Search Ads (Phoenix Nest) | [pdf] |
Click-Through-Rate(CTR) Prediction
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] |
AutoDis: An Embedding Learning Framework for Numerical Features in CTR Prediction | KDD ’21 | [pdf] |
Retrieval
Titile | Booktitle | Resources |
---|---|---|
DSSM:Learning Deep Structured Semantic Models for Web Search using Clickthrough Data | CIKM'13 | [pdf][TensorFlow] |
EBR:Embedding-based Retrieval in Facebook Search | KDD'20 | [pdf] |
Deep Retrieval: Learning A Retrievable Structure for Large-Scale Recommendations | arXiv'20 | [pdf] |
Sequence-based Recommendations
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] |
Knowledge Graph
Titile | Booktitle | Resources |
---|---|---|
RippleNet: RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systems | CIKM'2018 | [pdf] [code] |
Collaborative Filtering
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 |
AutoML
Titile | Booktitle | Resources |
---|---|---|
AutoCTR:Towards Automated Neural Interaction Discovery for Click-Through Rate Prediction | KDD'20 | [pdf] |
Other
DropoutNet: Addressing Cold Start in Recommender Systems. [pdf] [code]
Graph Neural Networks
Transfer Learning
Public Datasets
- KASANDR:KASANDR: A Large-Scale Dataset with Implicit Feedback for Recommendation (SIGIR 2017). [pdf] [KASANDR Data Set ]
Blogs
Courses & Tutorials
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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
Recommendation Systems Engineer Skill Tree
- Skill Tree pdf