{"type":"ScholarlyArticle","title":"Graph Contextualized Self-Attention Network for Session-based Recommendation","authors":[{"name":"Chengfeng Xu","slug":"chengfeng-xu","given":"Chengfeng","family":"Xu","affiliation":"Institute of AI , School of Computer Science and Technology , Soochow University , China; Zhejiang Lab , China"},{"name":"Pengpeng Zhao","slug":"pengpeng-zhao","given":"Pengpeng","family":"Zhao","affiliation":"Institute of AI , School of Computer Science and Technology , Soochow University , China; Zhejiang Lab , China; Key Lab of IIP of CAS , Institute of Computing Technology , Beijing , China"},{"name":"Yanchi Liu","slug":"yanchi-liu","given":"Yanchi","family":"Liu","affiliation":"Rutgers University , New Jersey , USA"},{"name":"Victor S. Sheng","slug":"victor-s-sheng","given":"Victor S.","family":"Sheng","affiliation":"The University of Central Arkansas , Conway , USA"},{"name":"Jiajie Xu","slug":"jiajie-xu","given":"Jiajie","family":"Xu","affiliation":"Institute of AI , School of Computer Science and Technology , Soochow University , China"},{"name":"Fuzhen Zhuang","slug":"fuzhen-zhuang","given":"Fuzhen","family":"Zhuang","affiliation":"Key Lab of IIP of CAS , Institute of Computing Technology , Beijing , China"},{"name":"Junhua Fang","slug":"junhua-fang","given":"Junhua","family":"Fang","affiliation":"Institute of AI , School of Computer Science and Technology , Soochow University , China"},{"name":"Xiaofang Zhou","slug":"xiaofang-zhou","given":"Xiaofang","family":"Zhou","affiliation":"The University of Queensland , Brisbane , Australia; Zhejiang Lab , China"}],"abstract":"Session-based recommendation, which aims to predict the user's immediate next action based on anonymous sessions, is a key task in many online services (e.g., e-commerce, media streaming).  Recently, Self-Attention Network (SAN) has achieved significant success in various sequence modeling tasks without using either recurrent or convolutional network. However, SAN lacks local dependencies that exist over adjacent items and limits its capacity for learning contextualized representations of items in sequences.  In this paper, we propose a graph contextualized self-attention model (GC-SAN), which utilizes both graph neural network and self-attention mechanism, for session-based recommendation. In GC-SAN, we dynamically construct a graph structure for session sequences and capture rich local dependencies via graph neural network (GNN).  Then each session learns long-range dependencies by applying the self-attention mechanism. Finally, each session is represented as a linear combination of the global preference and the current interest of that session. Extensive experiments on two real-world datasets show that GC-SAN outperforms state-of-the-art methods consistently.","doi":"10.24963/ijcai.2019/547","url":"https://ijcai.org/proceedings/2019/547","pdf":"https://ijcai.org/proceedings/2019/0547.pdf","markdown":"https://ijcai.org/proceedings/2019/547.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/547","volume":{"slug":"2019","title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2019","url":"https://ijcai.org/proceedings/2019/","year":2019,"location":"Macao","dates":"10-16 August 2019","editors":"Sarit Kraus","isbn":"978-0-9992411-4-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2019"},"section":"Machine Learning M-Z","track":"Main track","number":547,"pages":{"first":3940,"last":3946},"keywords":["Machine Learning: Recommender Systems","Machine Learning: Data Mining"],"videos":[],"references":[{"position":1,"raw":"[Battaglia et al., 2018] Peter W Battaglia, Jessica B Ham- rick, Victor Bapst, Alvaro Sanchez-Gonzalez, Vinicius Zambaldi, Mateusz Malinowski, Andrea Tacchetti, David Raposo, Adam Santoro, Ryan Faulkner, et al. Relational inductive biases, deep learning, and graph networks. arXiv preprint arXiv:1806.01261, 2018.","title":"Relational inductive biases, deep learning, and graph networks","authors":"Jessica B Peter W Battaglia, Victor Hamrick, Alvaro Bapst, Vinicius Sanchez-Gonzalez, Mateusz Zambaldi, Andrea Malinowski, David Tacchetti, Adam Raposo, Ryan Santoro, Faulkner","year":"2018","venue":null,"doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Bonnin and Jannach, 2015] Geoffray Bonnin and Dietmar Jannach. Automated generation of music playlists: Sur- vey and experiments. CSUR, 47(2):26, 2015.","title":"Automated generation of music playlists: Survey and experiments","authors":"Jannach Bonnin, Geoffray Bonnin, Dietmar Jannach","year":"2015","venue":"CSUR","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[He et al., 2016] Xiangnan He, Hanwang Zhang, Min-Yen Kan, and Tat-Seng Chua. Fast matrix factorization for on- line recommendation with implicit feedback. In SIGIR, pages 549-558. ACM, 2016.","title":"Fast matrix factorization for online recommendation with implicit feedback","authors":"He","year":"2016","venue":"SIGIR","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[He et al., 2018] Xiangnan He, Zhankui He, Jingkuan Song, Zhenguang Liu, Yu-Gang Jiang, and Tat-Seng Chua. NAIS: neural attentive item similarity model for recom- mendation. TKDE, 30(12):2354-2366, 2018.","title":"NAIS: neural attentive item similarity model for recommendation","authors":"He","year":"2018","venue":"TKDE","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Hidasi et al., 2016] Balázs Hidasi, Alexandros Karat- zoglou, Linas Baltrunas, and Domonkos Tikk. Session- based recommendations with recurrent neural networks. ICLR, 2016.","title":"Sessionbased recommendations with recurrent neural networks","authors":"Balázs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, Domonkos Tikk","year":"2016","venue":"ICLR","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Huang et al., 2018] Xiaowen Huang, Shengsheng Qian, Quan Fang, Jitao Sang, and Changsheng Xu. Csan: Con- textual self-attention network for user sequential recom- mendation. In ACM Multimedia, pages 447-455. ACM, 2018.","title":"Csan: Contextual self-attention network for user sequential recommendation","authors":"Xiaowen Huang, Shengsheng Qian, Quan Fang, Jitao Sang, Changsheng Xu","year":"2018","venue":"ACM Multimedia","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Kang and McAuley, 2018] Wang-Cheng Kang and Julian McAuley. Self-attentive sequential recommendation. In ICDM, pages 197-206. IEEE, 2018.","title":"Self-attentive sequential recommendation","authors":"Wang-Cheng Kang, Julian Mcauley","year":"2018","venue":"ICDM","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Li et al., 2017] Jing Li, Pengjie Ren, Zhumin Chen, Zhaochun Ren, Tao Lian, and Jun Ma. Neural attentive session-based recommendation. In CIKM, pages 1419- 1428. ACM, 2017.","title":"Neural attentive session-based recommendation","authors":"Jing Li, Pengjie Ren, Zhumin Chen, Zhaochun Ren, Tao Lian, Jun Ma","year":"2017","venue":"CIKM","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Li et al., 2018] Zhongyang Li, Xiao Ding, and Ting Liu. Constructing narrative event evolutionary graph for script event prediction. arXiv preprint arXiv:1805.05081, 2018.","title":"Constructing narrative event evolutionary graph for script event prediction","authors":"Zhongyang Li, Xiao Ding, Ting Liu","year":"2018","venue":null,"doi":null,"url":null,"internal_url":"/proceedings/2018/584"},{"position":10,"raw":"[Li et al., 2019] Xiangpeng Li, Jingkuan Song, Lianli Gao, Xianglong Liu, Wenbing Huang, Xiangnan He, and Chuang Gan. Beyond rnns: Positional self-attention with co-attention for video question answering. In AAAI, 2019.","title":"Beyond rnns: Positional self-attention with co-attention for video question answering","authors":"Li","year":"2019","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Lin et al., 2017] Zhouhan Lin, Minwei Feng, Cicero Nogueira dos Santos, Mo Yu, Bing Xiang, Bowen Zhou, and Yoshua Bengio. A structured self-attentive sentence embedding. In ICLR, 2017.","title":"A structured self-attentive sentence embedding","authors":"Lin","year":"2017","venue":"ICLR","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Liu et al., 2018] Qiao Liu, Yifu Zeng, Refuoe Mokhosi, and Haibin Zhang. Stamp: short-term attention/memory pri- ority model for session-based recommendation. In KDD, pages 1831-1839. ACM, 2018.","title":"Stamp: short-term attention/memory priority model for session-based recommendation","authors":"Qiao Liu, Yifu Zeng, Refuoe Mokhosi, Haibin Zhang","year":"2018","venue":"KDD","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Liu et al., 2019] Pengfei Liu, Shuaichen Chang, Xuanjing Huang, Jian Tang, and Jackie Chi Kit Cheung. Contex- tualized non-local neural networks for sequence learning. AAAI, 2019. [Marino et al., 2017] Kenneth Marino, Ruslan Salakhutdi- nov, and Abhinav Gupta. The more you know: Using knowledge graphs for image classification. CVPR, pages 20-28, 2017.","title":"The more you know: Using knowledge graphs for image classification","authors":"Pengfei Liu, Shuaichen Chang, Xuanjing Huang, Jian Tang, Jackie Chi, Kit Cheung","year":"2017","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Rendle et al., 2009] Steffen Rendle, Christoph Freuden- thaler, Zeno Gantner, and Lars Schmidt-Thieme. Bpr: Bayesian personalized ranking from implicit feedback. In UAI, pages 452-461. AUAI Press, 2009.","title":"Bpr: Bayesian personalized ranking from implicit feedback","authors":"Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, Lars Schmidt-Thieme","year":"2009","venue":"UAI","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Rendle et al., 2010] Steffen Rendle, Christoph Freuden- thaler, and Lars Schmidt-Thieme. Factorizing personal- ized markov chains for next-basket recommendation. In WWW, pages 811-820. ACM, 2010.","title":"Factorizing personalized markov chains for next-basket recommendation","authors":"Steffen Rendle, Christoph Freudenthaler, Lars Schmidt-Thieme","year":"2010","venue":"WWW","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Sarwar et al., 2001] Badrul Sarwar, George Karypis, Joseph Konstan, and John Riedl. Item-based collaborative filter- ing recommendation algorithms. In WWW, pages 285- 295. ACM, 2001.","title":"Item-based collaborative filtering recommendation algorithms","authors":"Badrul Sarwar, George Karypis, Joseph Konstan, John Riedl","year":"2001","venue":"WWW","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Scarselli et al., 2009] Franco Scarselli, Marco Gori, Ah Chung Tsoi, Markus Hagenbuchner, and Gabriele Monfardini. The graph neural network model. IEEE Transactions on Neural Networks, 20(1):61-80, 2009.","title":"The graph neural network model","authors":"Franco Scarselli, Marco Gori, Ah Chung Tsoi, Markus Hagenbuchner, Gabriele Monfardini","year":"2009","venue":"IEEE Transactions on Neural Networks","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Tan et al., 2016] Yong Kiam Tan, Xinxing Xu, and Yong Li- u. Improved recurrent neural networks for session-based recommendations. In RecSys, pages 17-22. ACM, 2016.","title":"Improved recurrent neural networks for session-based recommendations","authors":"Yong Kiam Tan, Xinxing Xu, Yong Liu","year":"2016","venue":"RecSys","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Vaswani et al., 2017] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. Attention is all you need. In NIPS, pages 5998-6008, 2017.","title":"Attention is all you need","authors":"Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, Illia Polosukhin","year":"2017","venue":"NIPS","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Wang et al., 2019] Xiang Wang, Xiangnan He, Meng Wang, Fuli Feng, and Tat-Seng Chua. Neural graph col- laborative filtering. In SIGIR, 2019.","title":"Neural graph collaborative filtering","authors":"Xiang Wang, Xiangnan He, Meng Wang, Fuli Feng, Tat-Seng Chua","year":"2019","venue":"SIGIR","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Wu et al., 2018] Shu Wu, Yuyuan Tang, Yanqiao Zhu, Liang Wang, Xing Xie, and Tieniu Tan. Session-based recommendation with graph neural networks. AAAI, 2018.","title":"Session-based recommendation with graph neural networks","authors":"Shu Wu, Yuyuan Tang, Yanqiao Zhu, Liang Wang, Xing Xie, Tieniu Tan","year":"2018","venue":null,"doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Yang et al., 2018] Baosong Yang, Zhaopeng Tu, Derek F Wong, Fandong Meng, Lidia S Chao, and Tong Zhang. Modeling localness for self-attention networks. EMNLP, 2018.","title":"Modeling localness for self-attention networks","authors":"Yang","year":"2018","venue":"EMNLP","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Yuan et al., 2019] Fajie Yuan, Alexandros Karatzoglou an- dIoannis Arapakis, Joemon M. Jose, and Xiangnan He. A simple convolutional generative network for next item rec- ommendation. In WSDM, pages 582-590, 2019.","title":"Xiangnan He. A simple convolutional generative network for next item recommendation","authors":"Yuan","year":"2019","venue":"WSDM","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Zhao et al., 2019] Pengpeng Zhao, Haifeng Zhu, Yanchi Li- u, Jiajie Xu, Zhixu Li, Fuzhen Zhuang, Victor S. Sheng, and Xiaofang Zhou. Where to go next: A spatio-temporal gated network for next poi recommendation. In AAAI, 2019.","title":"Where to go next: A spatio-temporal gated network for next poi recommendation","authors":"Pengpeng Zhao, Haifeng Zhu, Yanchi Liu, Jiajie Xu, Zhixu Li, Fuzhen Zhuang, Victor S Sheng, Xiaofang Zhou","year":"2019","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Zhou et al., 2018] Chang Zhou, Jinze Bai, Junshuai Song, Xiaofei Liu, Zhengchao Zhao, Xiusi Chen, and Jun Gao. Atrank: An attention-based user behavior modeling frame- work for recommendation. In AAAI, 2018.","title":"Atrank: An attention-based user behavior modeling framework for recommendation","authors":"Chang Zhou, Jinze Bai, Junshuai Song, Xiaofei Liu, Zhengchao Zhao, Xiusi Chen, Jun Gao","year":"2018","venue":"AAAI","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}