I will maintain https://github.com/AIM-SE/PR4Rec instead of this page.

Survey Papers

survey papers

Problems

Heterogenous RecSys

=

Session based RecSys

Based on Attention Model

=

Based on Neural Language Model

= Kang, Wang-Cheng, and Julian McAuley. 2018. “Self-Attentive Sequential Recommendation.” In IEEE International Conference on Data Mining. http://arxiv.org/abs/1808.09781.

• Approach : based on Transformer
• Dataset : Amazon Beauty, Amazon Games, Steam, MovieLens-1M
• github : https://github.com/kang205/SASRec

= Anh, Pham Hoang, Ngo Xuan Bach, and Tu Minh Phuong. 2019. “Session-Based Recommendation with Self-Attention.” In Proceedings of the Tenth International Symposium on Information and Communication Technology, 1–8. SoICT 2019. New York, NY, USA: Association for Computing Machinery.

• Approach : based on dual Transformers
• Dataset : Beauty, Steam ML-1m, ML-20m
• github :

= Sun, Fei, Jun Liu, Jian Wu, Changhua Pei, Xiao Lin, Wenwu Ou, and Peng Jiang. 2019. “BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer.” In Proceedings of the 28th ACM International Conference on Information and Knowledge Management, 1441–50. CIKM ’19. New York, NY, USA: Association for Computing Machinery.

• Approach : based on BERT
• Dataset : Beauty, Steam ML-1m, ML-20m
• github : https://github.com/FeiSun/BERT4Rec

DataSets

= List of Datasets

• https://github.com/daicoolb/RecommenderSystem-DataSet
• https://cseweb.ucsd.edu/~jmcauley/datasets.html

= YOOCHOOSE (Recsys 2015)

• http://2015.recsyschallenge.com/challenge.html
• https://www.kaggle.com/chadgostopp/recsys-challenge-2015?select=dataset-README.txt

= Digitica (CKIM 2016)

• http://cikm2016.cs.iupui.edu/cikm-cup
• https://competitions.codalab.org/competitions/11161#learn_the_details-data2

= Taobao/Tmall (IJCAI16)

• User Behavior Data on Taobao/Tmall IJCAI16 Contest
• https://tianchi.aliyun.com/dataset/dataDetail?dataId=53
• https://tianchi.aliyun.com/dataset/dataDetail?dataId=649

= Amazon Beautry

• http://jmcauley.ucsd.edu/data/amazon/

= Steam

• https://cseweb.ucsd.edu/~jmcauley/datasets.html#steam_data

= Movie Lens

• 1M : https://grouplens.org/datasets/movielens/1m/
• 20M : https://grouplens.org/datasets/movielens/20m/

Baseline Codes

= AttRec, Caser, GRU4Rec, FPMC, TransRec, SASRec

• (Tensorflow 1.1+) https://github.com/slientGe/Sequential_Recommendation_Tensorflow

= NCF

• (Keras) https://github.com/hexiangnan/neural_collaborative_filtering

= Caser

• (pytorch) https://github.com/graytowne/caser_pytorch

Useful Links

= Full papers related to the Recommender System from SIGIR 2020

• https://github.com/jihoo-kim/RecSys-Papers-from-SIGIR-2020