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] |

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] |

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 |

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

• Deep Learning Meets Recommendation Systems by Wann-Jiun

• Quora: Has there been any work on using deep learning for recommendation engines?.

Courses & Tutorials

• 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

• Skill Tree pdf