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] |
| 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] |
| 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] |
| 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 |
| Titile |
Booktitle |
Resources |
| AutoCTR:Towards Automated Neural Interaction Discovery for Click-Through Rate Prediction |
KDD'20 |
[pdf] |
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 ]
-
Recommender Systems Specialization Coursera
-
Deep Learning for Recommender Systems by Balázs Hidasi. RecSys Summer School, 21-25 August, 2017, Bozen-Bolzano. Slides
-
Deep Learning for Recommender Systems by Alexandros Karatzoglou and Balázs Hidasi. RecSys2017 Tutorial. Slides
Recommendation Systems Engineer Skill Tree
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