ABSTRACT
Users’ social connections have recently shown significant benefits to session-based recommendations, and graph neural networks have demonstrated great success in learning the pattern of information flow among users. However, the current paradigm presumes a given social network, which is not necessarily consistent with the fast-evolving shared interests and is expensive to collect. We propose a novel idea to learn the graph structure among users and make recommendations collectively in a coupled framework. This idea raises two challenges, i.e., scalability and effectiveness. We introduce a novel graph-structure learning framework for session-based recommendations (GSL4Rec) for solving both challenges simultaneously. Our framework has a two-stage strategy, i.e., the coarse neighbor screening and the self-adaptive graph structure learning, to enable the exploration of potential links among all users while maintaining a tractable amount of computation for scalability. We also propose a phased heuristic learning strategy to sequentially and synergistically train the graph learning part and recommendation part of GSL4Rec, thus improving the effectiveness by making the model easier to achieve good local optima. Experiments on five public datasets demonstrate that our proposed model significantly outperforms strong baselines, including state-of-the-art social network-based methods.
- Sunil Arya, David M Mount, Nathan S Netanyahu, Ruth Silverman, and Angela Y Wu. 1998. An optimal algorithm for approximate nearest neighbor searching fixed dimensions. Journal of the ACM (JACM) 45, 6 (1998), 891–923.Google ScholarDigital Library
- James Atwood and Don Towsley. 2016. Diffusion-convolutional neural networks. In Advances in neural information processing systems. 1993–2001.Google Scholar
- Joan Bruna, Wojciech Zaremba, Arthur Szlam, and Yann LeCun. 2014. Spectral Networks and Locally Connected Networks on Graphs. In Proceedings of the 2nd International Conference on Learning Representations, ICLR 2014.Google Scholar
- Tianwen Chen and Raymond Chi-Wing Wong. 2020. Handling information loss of graph neural networks for session-based recommendation. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 1172–1180.Google ScholarDigital Library
- Edward Choi, Zhen Xu, Yujia Li, Michael Dusenberry, Gerardo Flores, Emily Xue, and Andrew Dai. 2020. Learning the graphical structure of electronic health records with graph convolutional transformer. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 34. 606–613.Google ScholarCross Ref
- Wenqi Fan, Yao Ma, Qing Li, Yuan He, Eric Zhao, Jiliang Tang, and Dawei Yin. 2019. Graph neural networks for social recommendation. In The World Wide Web Conference. 417–426.Google ScholarDigital Library
- Pan Gu, Yuqiang Han, Wei Gao, Guandong Xu, and Jian Wu. 2021. Enhancing session-based social recommendation through item graph embedding and contextual friendship modeling. Neurocomputing 419(2021), 190–202.Google ScholarCross Ref
- Balázs Hidasi and Alexandros Karatzoglou. 2018. Recurrent neural networks with top-k gains for session-based recommendations. In Proceedings of the 27th ACM international conference on information and knowledge management. 843–852.Google ScholarDigital Library
- Balázs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, and Domonkos Tikk. 2016. Session-based Recommendations with Recurrent Neural Networks. In The 4th International Conference on Learning Representations, ICLR 2016.Google Scholar
- Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long short-term memory. Neural computation 9, 8 (1997), 1735–1780.Google ScholarDigital Library
- Liang Hu, Longbing Cao, Shoujin Wang, Guandong Xu, Jian Cao, and Zhiping Gu. 2017. Diversifying Personalized Recommendation with User-session Context. In Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence. 1858–1864.Google ScholarCross Ref
- Walid Krichene and Steffen Rendle. 2020. On sampled metrics for item recommendation. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 1748–1757.Google ScholarDigital Library
- Jing Li, 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. 1419–1428.Google ScholarDigital Library
- Yaguang Li, Rose Yu, Cyrus Shahabi, and Yan Liu. 2018. Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting. In Proceddings of the 6th International Conference on Learning Representations.Google Scholar
- Haozhe Lin, Yushun Fan, Jia Zhang, and Bing Bai. 2021. REST: Reciprocal Framework for Spatiotemporal-coupled Predictions. In Proceedings of the Web Conference 2021. 3136–3145.Google ScholarDigital Library
- Malte Ludewig and Dietmar Jannach. 2018. Evaluation of session-based recommendation algorithms. User Modeling and User-Adapted Interaction 28, 4-5 (2018), 331–390.Google ScholarDigital Library
- Hao Ma, Dengyong Zhou, Chao Liu, Michael R Lyu, and Irwin King. 2011. Recommender systems with social regularization. In Proceedings of the fourth ACM international conference on Web search and data mining. 287–296.Google ScholarDigital Library
- Mathias Niepert, Mohamed Ahmed, and Konstantin Kutzkov. 2016. Learning Convolutional Neural Networks for Graphs. In Proceedings of the 33nd International Conference on Machine Learning. PMLR, 2014–2023.Google Scholar
- Utpala Niranjan, RBV Subramanyam, and V Khanaa. 2010. Developing a web recommendation system based on closed sequential patterns. In International Conference on Advances in Information and Communication Technologies. Springer, 171–179.Google ScholarCross Ref
- Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, 2019. Pytorch: An imperative style, high-performance deep learning library. Advances in neural information processing systems 32, 8026–8037.Google Scholar
- Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2009. BPR: Bayesian Personalized Ranking from Implicit Feedback. In Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence. 452–461.Google ScholarDigital Library
- Liqiang Song, Ye Bi, Mengqiu Yao, Zhenyu Wu, Jianming Wang, and Jing Xiao. 2020. DREAM: A Dynamic Relation-Aware Model for Social Recommendation. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management. 2225–2228.Google ScholarDigital Library
- Weiping Song, Zhiping Xiao, Yifan Wang, Laurent Charlin, Ming Zhang, and Jian Tang. 2019. Session-based social recommendation via dynamic graph attention networks. In Proceedings of the Twelfth ACM international conference on web search and data mining. 555–563.Google ScholarDigital Library
- Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. 2018. Graph Attention Networks. In 6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, April 30 - May 3, 2018, Conference Track Proceedings.Google Scholar
- Shoujin Wang, Longbing Cao, Yan Wang, Quan Z Sheng, Mehmet A Orgun, and Defu Lian. 2021. A survey on session-based recommender systems. ACM Computing Surveys (CSUR) 54, 7 (2021), 1–38.Google ScholarDigital Library
- Xiang Wu, Qi Liu, Enhong Chen, Liang He, Jingsong Lv, Can Cao, and Guoping Hu. 2013. Personalized next-song recommendation in online karaokes. In Proceedings of the 7th ACM Conference on Recommender Systems. 137–140.Google ScholarDigital Library
- Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, and S Yu Philip. 2020. A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32, 1(2020), 4–24.Google ScholarCross Ref
- Zonghan Wu, Shirui Pan, Guodong Long, Jing Jiang, Xiaojun Chang, and Chengqi Zhang. 2020. Connecting the dots: Multivariate time series forecasting with graph neural networks. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 753–763.Google ScholarDigital Library
- Z Wu, S Pan, G Long, J Jiang, and C Zhang. 2019. Graph WaveNet for Deep Spatial-Temporal Graph Modeling. In The 28th International Joint Conference on Artificial Intelligence. 1907–1913.Google Scholar
- Ghim-Eng Yap, Xiao-Li Li, and S Yu Philip. 2012. Effective next-items recommendation via personalized sequential pattern mining. In International conference on database systems for advanced applications. 48–64.Google ScholarDigital Library
- Qi Zhang, Jianlong Chang, Gaofeng Meng, Shiming Xiang, and Chunhong Pan. 2020. Spatio-temporal graph structure learning for traffic forecasting. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 34. 1177–1185.Google ScholarCross Ref
- Tong Zhao, Julian McAuley, and Irwin King. 2014. Leveraging social connections to improve personalized ranking for collaborative filtering. In Proceedings of the 23rd ACM international conference on conference on information and knowledge management. 261–270.Google ScholarDigital Library
Index Terms
- GSL4Rec: Session-based Recommendations with Collective Graph Structure Learning and Next Interaction Prediction
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