- [Selected papers in ICLR2023]
- [Selected papers in KDD2021]
- [Selected papers in SIGIR2021]
- [Selected papers in ICML2021]
- [Selected papers in AAAI2021]
- [Selected papers in WSDM2021]
- [Selected papers in WWW2021]
- [Selected papers in AAAI2020]
- [Selected papers in KDD2020]
- [Selected papers in SIGIR2020]
- [Cold-Start papers in RecSys]
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Pan, Zhiqiang, Fei Cai, Yanxiang Ling, and Maarten de Rijke. 2020. “Rethinking Item Importance in Session-Based Recommendation.” In SIGIR. [Link]
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Chen, T., and R. C. Wong. 2019. “Session-Based Recommendation with Local Invariance.” In 2019 IEEE International Conference on Data Mining (ICDM), 994–99.
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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]
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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.
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Zhang, S., Y. Tay, L. Yao, and A. Sun. 2018. “Next Item Recommendation with Self-Attention.” arXiv.” Information Retrieval.
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Wang, Tian, and Kyunghyun Cho. 2017. “Attention-Based Mixture Density Recurrent Networks for History-Based Recommendation.” arXiv [cs.LG]. arXiv. [Link]
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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.
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]
- 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]
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Heterogeneous Information Network Embedding: Methods and Implements : [LINK]
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PyTorch geometric : [LINK]
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dgl(PyTorch, MXNet, TensorFlow) : [LINK]
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stellagraph : [LINK]
- 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)
- https://github.com/daicoolb/RecommenderSystem-DataSet
- https://cseweb.ucsd.edu/~jmcauley/datasets.html
- https://github.com/caserec/Datasets-for-Recommender-Systems
- http://konect.cc/
- http://2015.recsyschallenge.com/challenge.html
- https://www.kaggle.com/chadgostopp/recsys-challenge-2015?select=dataset-README.txt
- http://cikm2016.cs.iupui.edu/cikm-cup
- https://competitions.codalab.org/competitions/11161#learn_the_details-data2
- User Behavior Data on Taobao/Tmall IJCAI16 Contest
- https://tianchi.aliyun.com/dataset/dataDetail?dataId=53
- https://tianchi.aliyun.com/dataset/dataDetail?dataId=649
- 1M : https://grouplens.org/datasets/movielens/1m/
- 20M : https://grouplens.org/datasets/movielens/20m/
- AttRec, Caser, GRU4Rec, FPMC, TransRec, SASRec [CODE:Tensorflow 1.1+]
- NCF [CODE:keras]
- Caser [CODE:pytorch]