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Table of Contents

  1. New
  2. Survey Papers
  3. Problems/Approaches
  4. DataSets
  5. Baseline Codes
  6. Wanna more papres?

New

Survey Papers

Problems/Approaches

Session based RecSys

Based on Attention Model

  • Pan, Zhiqiang, Fei Cai, Yanxiang Ling, and Maarten de Rijke. 2020. “Rethinking Item Importance in Session-Based Recommendation.” In SIGIR. [Link]

  • Chen, T., and R. C. Wong. 2019. “Session-Based Recommendation with Local Invariance.” In 2019 IEEE International Conference on Data Mining (ICDM), 994–99.

  • Wu, Shu, Mengqi Zhang, Xin Jiang, Xu Ke, and Liang Wang. 2019. “Personalizing Graph Neural Networks with Attention Mechanism for Session-Based Recommendation.” IEEE Transactions on Knowledge and Data Engineering 31 (9). [Link]

  • Xu, Chengfeng, Pengpeng Zhao, Yanchi Liu, Victor S. Sheng, Jiajie Xu, Fuzhen Zhuang, Junhua Fang, and Xiaofang Zhou. 2019. “Graph Contextualized Self-Attention Network for Session-Based Recommendation.” In Proc. 28th Int. Joint Conf. Artif. Intell.(IJCAI), 3940–46. pdfs.semanticscholar.org.

  • Zhang, S., Y. Tay, L. Yao, and A. Sun. 2018. “Next Item Recommendation with Self-Attention.” arXiv.” Information Retrieval.

  • Wang, Tian, and Kyunghyun Cho. 2017. “Attention-Based Mixture Density Recurrent Networks for History-Based Recommendation.” arXiv [cs.LG]. arXiv. [Link]

  • Li, Jing, Pengjie Ren, Zhumin Chen, Zhaochun Ren, Tao Lian, and Jun Ma. 2017. “Neural Attentive Session-Based Recommendation.” In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, 1419–28. CIKM ’17. New York, NY, USA: Association for Computing Machinery.

Based on Neural Language Model

Zhang, Yuanxing, Pengyu Zhao, Yushuo Guan, Lin Chen, Kaigui Bian, Lingyang Song, Bin Cui, and Xiaoming Li. 2020. “Preference-Aware Mask for Session-Based Recommendation with Bidirectional Transformer.” ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). doi:10.1109/icassp40776.2020.9054639.

  • Approach : based on Transformers with mask considering user's preference
  • Dataset : LastFM, ML-20m, ML-YOOCHOOSE
  • 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
  • [CODE]

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 :

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
  • [CODE]

Heterogenous RecSys

  • MAGNN: Metapath Aggregated Graph Neural Network for Heterogeneous Graph Embedding. (WWW`20) [Link]
  • Heterogeneous Graph Neural Network. (KDD`19) [Link]
  • Tripartite Heterogeneous Graph Propagation for Large-Scale Social Recommendation. (RecSys`19) [Link]
  • Learning Disentangled Representations for Recommendation. (NeurIPS`19) [Link]
  • Evolutionarily Learning Multi-Aspect Interactions and Influences from Network Structure and Node Content.” (AAAI`19) [Link]
  • Heterogeneous Information Network Embedding for Recommendation.” (TKDE`19) [LINK]

Implementaions

  • Heterogeneous Information Network Embedding: Methods and Implements : [LINK]

  • PyTorch geometric : [LINK]

  • dgl(PyTorch, MXNet, TensorFlow) : [LINK]

  • stellagraph : [LINK]

ETC

  • Zhao, Pengyu, Kecheng Xiao, Yuanxing Zhang, Kaigui Bian, and Wei Yan. 2020. “AMER: Automatic Behavior Modeling and Interaction Exploration in Recommender System.” arXiv [cs.LG]. arXiv. [Link] (They argued that their model outperforms BERT4Rec)

DataSets

List of Datasets

YOOCHOOSE (Recsys 2015)

Digitica (CKIM 2016)

Taobao/Tmall (IJCAI16)

Amazon Beautry

Steam

Movie Lens

Baseline Codes

Wanna more papres?

Conferences

RecSys Best Papper : [LINK]

RecSys Challenge : [2020] [2019] [2018] [2017] [2016]

Etc.

Must-read papers on Recommender System [LINK]

links to conference publications in graph-based deep learning [LINK]

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