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Papers on Recommendation Systems

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28 March 2021


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Papers on Recommendation Systems

Papers and works on Recommendation Systems(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

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

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