ABSTRACT
Session-based recommendation (SBR) is a challenging task, which aims at recommending items based on anonymous behavior sequences. Almost all the existing solutions for SBR model user preference only based on the current session without exploiting the other sessions, which may contain both relevant and irrelevant item-transitions to the current session. This paper proposes a novel approach, called Global Context Enhanced Graph Neural Networks (GCE-GNN) to exploit item transitions over all sessions in a more subtle manner for better inferring the user preference of the current session. Specifically, GCE-GNN learns two levels of item embeddings from session graph and global graph, respectively: (i) Session graph, which is to learn the session-level item embedding by modeling pairwise item-transitions within the current session; and (ii) Global graph, which is to learn the global-level item embedding by modeling pairwise item-transitions over all sessions. In GCE-GNN, we propose a novel global-level item representation learning layer, which employs a session-aware attention mechanism to recursively incorporate the neighbors' embeddings of each node on the global graph. We also design a session-level item representation learning layer, which employs a GNN on the session graph to learn session-level item embeddings within the current session. Moreover, GCE-GNN aggregates the learnt item representations in the two levels with a soft attention mechanism. Experiments on three benchmark datasets demonstrate that GCE-GNN outperforms the state-of-the-art methods consistently.
Supplemental Material
- Will Hamilton, Zhitao Ying, and Jure Leskovec. 2017. Inductive representation learning on large graphs. In NIPS. 1024--1034.Google Scholar
- Balázs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, and Domonkos Tikk. 2016. Session-based recommendations with recurrent neural networks. In ICLR.Google Scholar
- Dietmar Jannach and Malte Ludewig. 2017. When recurrent neural networks meet the neighborhood for session-based recommendation. In RecSys. 306--310.Google Scholar
- Wang-Cheng Kang and Julian McAuley. 2018. Self-attentive sequential recommendation. In ICDM. 197--206.Google Scholar
- Thomas N Kipf and Max Welling. 2017. Semi-supervised classification with graph convolutional networks. In ICLR.Google Scholar
- Jing Li, Pengjie Ren, Zhumin Chen, Zhaochun Ren, Tao Lian, and Jun Ma. 2017. Neural attentive session-based recommendation. In CIKM. 1419--1428.Google Scholar
- Yujia Li, Daniel Tarlow, Marc Brockschmidt, and Richard Zemel. 2016. Gated graph sequence neural networks. In ICLR.Google Scholar
- Qiao Liu, Yifu Zeng, Refuoe Mokhosi, and Haibin Zhang. 2018. STAMP: short-term attention/memory priority model for session-based recommendation. In SIGKDD. 1831--1839.Google Scholar
- Ruihong Qiu, Jingjing Li, Zi Huang, and Hongzhi Yin. 2019. Rethinking the Item Order in Session-based Recommendation with Graph Neural Networks. In CIKM. 579--588.Google Scholar
- Steffen Rendle, Christoph Freudenthaler, and Lars Schmidt-Thieme. 2010. Factorizing personalized markov chains for next-basket recommendation. In WWW. 811--820.Google Scholar
- Badrul Munir Sarwar, George Karypis, Joseph A Konstan, John Riedl, et almbox. 2001. Item-based collaborative filtering recommendation algorithms.. In WWW. 285--295.Google Scholar
- Guy Shani, David Heckerman, and Ronen I Brafman. 2005. An MDP-based recommender system. JMLR, 1265--1295.Google Scholar
- Jing Song, Hong Shen, Zijing Ou, Junyi Zhang, Teng Xiao, and Shangsong Liang. 2019. ISLF: Interest Shift and Latent Factors Combination Model for Session-based Recommendation. In IJCAI. 5765--5771.Google Scholar
- Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. 2014. Dropout: a simple way to prevent neural networks from overfitting. JMLR (2014), 1929--1958.Google Scholar
- Yong Kiam Tan, Xinxing Xu, and Yong Liu. 2016. Improved recurrent neural networks for session-based recommendations. In Proceedings of the 1st Workshop on Deep Learning for Recommender Systems. 17--22.Google ScholarDigital Library
- Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In NIPS. 5998--6008.Google Scholar
- Petar Velivc ković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. 2018. Graph attention networks. In ICLR.Google Scholar
- Huizhao Wang, Guanfeng Liu, An Liu, Zhixu Li, and Kai Zheng. 2019 e. DMRAN:A Hierarchical Fine-Grained Attention-Based Network for Recommendation. In IJCAI. 3698--3704.Google Scholar
- Meirui Wang, Pengjie Ren, Lei Mei, Zhumin Chen, Jun Ma, and Maarten de Rijke. 2019 f. A Collaborative Session-based Recommendation Approach with Parallel Memory Modules. In SIGIR.Google Scholar
- Shoujin Wang, Liang Hu, Yan Wang, Longbing Cao, Quan Z. Sheng, and Mehmet Orgun. 2019 b. Sequential Recommender Systems: Challenges, Progress and Prospects. In IJCAI. 6332--6338.Google Scholar
- Shoujin Wang, Liang Hu, Yan Wang, Quan Z. Sheng, Mehmet Orgun, and Longbing Cao. 2019 c. Modeling Multi-Purpose Sessions for Next-Item Recommendations via Mixture-Channel Purpose Routing Networks. In IJCAI. 3771--3777.Google Scholar
- Xiang Wang, Xiangnan He, Meng Wang, Fuli Feng, and Tat-Seng Chua. 2019 a. Neural Graph Collaborative Filtering. In SIGIR.Google Scholar
- Xiao Wang, Houye Ji, Chuan Shi, Bai Wang, Yanfang Ye, Peng Cui, and Philip S Yu. 2019 d. Heterogeneous Graph Attention Network. In WWW. 2022--2032.Google Scholar
- Shu Wu, Yuyuan Tang, Yanqiao Zhu, Liang Wang, Xing Xie, and Tieniu Tan. 2019. Session-based recommendation with graph neural networks. In AAAI. 346--353.Google Scholar
- Chengfeng Xu, 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 IJCAI. 3940--3946.Google Scholar
- Eva Zangerle, Martin Pichl, Wolfgang Gassler, and Günther Specht. 2014. #nowplaying Music Dataset: Extracting Listening Behavior from Twitter. In ISMM. 21--26.Google Scholar
Index Terms
- Global Context Enhanced Graph Neural Networks for Session-based Recommendation
Recommendations
Heterogeneous Global Graph Neural Networks for Personalized Session-based Recommendation
WSDM '22: Proceedings of the Fifteenth ACM International Conference on Web Search and Data MiningPredicting the next interaction of a short-term interaction session is a challenging task in session-based recommendation. Almost all existing works rely on item transition patterns, and neglect user historical sessions while modeling user preference, ...
Attentive Capsule Graph Neural Networks for Session-Based Recommendation
Knowledge Science, Engineering and ManagementAbstractConsidering recommendation scenarios in that user profiles are anonymous, the session-based recommendation is proposed to predict the items users are interested in from short sessions. However, most existing methods for session-based ...
Session-based recommendation with time-aware neural attention network
Highlights- Propose a session-based recommendation model based on graph attention mechanism.
AbstractIn situations where user information and detailed knowledge of user behaviors are challenging to obtain, the session-based recommendation is essential. The session-based recommendation (SBR) relies on the current anonymous session ...
Comments