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 MachinesICDM'2010
[pdf] [code] [tffm] [fmpytorch]
libFM : Factorization Machines with libFMACM Trans'2012
[pdf] [code]
GBDT+LR : Practical Lessons from Predicting Clicks on Ads at FacebookADKDD'14
[pdf]
FFM : Field-aware Factorization Machines for CTR PredictionRecSys'2016
[pdf] [code]
FNN : Deep Learning over Multi-field Categorical Data: A Case Study on User Response PredictionECIR'2016
[pdf] [Tensorflow]
PNN : Product-based Neural Networks for User Response PredictionICDM'2016
[pdf] [Tensorflow]
Wide&Deep : Wide & Deep Learning for Recommender SystemsDLRS'2016
[pdf] [Tensorflow] [Blog]
AFM : Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention NetworksIJCAI'2017
[pdf] [Tensorflow]
NFM : Neural Factorization Machines for Sparse Predictive AnalyticsSIGIR'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 PredictionsADKDD'2017
[pdf] [Keras] [Tensorflow]
xDeepFM : xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender SystemsKDD'2018
[pdf] [Tensorflow]
DIN : DIN: Deep Interest Network for Click-Through Rate PredictionKDD'2018
[pdf] [Tensorflow]
DIEN : DIEN: Deep Interest Evolution Network for Click-Through Rate PredictionAAAI'2019
[pdf] [Tensorflow]
DSIN : Deep Session Interest Network for Click-Through Rate PredictionIJCAI'2019
[pdf] [Tensorflow]
AutoInt : Automatic Feature Interaction Learning via Self-Attentive Neural NetworksCIKM'2019
[pdf] [Tensorflow]
FiBiNET : Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate PredictionRecSys '19
[pdf] [Tensorflow]
DeepGBM :A Deep Learning Framework Distilled by GBDT for Online Prediction TasksKDD'2019
[pdf] [Tensorflow]
FLEN : Leveraging Field for Scalable CTR PredictionAAAI'2020
[pdf] [Tensorflow]
DFN : Deep Feedback Network for RecommendationIJCAI'2020
[pdf] [Tensorflow]
AutoDis : An Embedding Learning Framework for Numerical Features in CTR PredictionKDD ’21
[pdf]
Titile
Booktitle
Resources
DSSM :Learning Deep Structured Semantic Models for Web Search using Clickthrough DataCIKM'13
[pdf] [TensorFlow]
EBR :Embedding-based Retrieval in Facebook SearchKDD'20
[pdf]
Deep Retrieval : Learning A Retrievable Structure for Large-Scale RecommendationsarXiv'20
[pdf]
Sequence-based Recommendations
Titile
Booktitle
Resources
GRU4Rec :Session-based Recommendations with Recurrent Neural NetworksICLR'2016
[pdf] [code]
DREAM :A Dynamic Recurrent Model for Next Basket RecommendationSIGIR'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-LSTMIJCAI'2017
[pdf] [code]
Caser :Personalized Top-N Sequential Recommendation via Convolutional Sequence EmbeddingCaserWSDM'2018
[pdf] [code]
SASRec :Self-Attentive Sequential RecommendationICDM'2018
[pdf] [code]
BERT4Rec :BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from TransformerACM WOODSTOCK’2019
[pdf] [code]
SR-GNN : Session-based Recommendation with Graph Neural NetworksAAAI'2019
[pdf] [code]
Titile
Booktitle
Resources
RippleNet : RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender SystemsCIKM'2018
[pdf] [code]
Titile
Booktitle
Resources
UBCF :GroupLens: an open architecture for collaborative filtering of netnewsCSCW'1994
[pdf] [code]
IBCF :Item-based collaborative filtering recommendation algorithmsWWW'2001
[pdf] [code]
SVD :Matrix Factorization Techniques for Recommender SystemsJournal Computer'2009
[pdf] [code]
SVD++ :Factorization meets the neighborhood: a multifaceted collaborative filtering modelKDD'2008
[pdf] [code]
PMF : Probabilistic Matrix FactorizationNIPS'2007
[pdf] [code]
CDL :Collaborative Deep Learning for Recommender SystemsKDD '2015
[pdf] [code] [PPT]
ConvMF :Convolutional Matrix Factorization for Document Context-Aware RecommendationRecSys'2016
[pdf] [code] [zhihu] [PPT]
NCF :Neural Collaborative FilteringWWW '17
pdf code
Titile
Booktitle
Resources
AutoCTR :Towards Automated Neural Interaction Discovery for Click-Through Rate PredictionKDD'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 