Ruihong Qiu
Hongzhi Yin
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Abstract
Different from the traditional recommender system, the session-based recommender system introduces the concept of the session, i.e., a sequence of interactions between a user and multiple items within a period, to preserve the user’s recent interest. The existing work on the session-based recommender system mainly relies on mining sequential patterns within individual sessions, which are not expressive enough to capture more complicated dependency relationships among items. In addition, it does not consider the cross-session information due to the anonymity of the session data, where the linkage between different sessions is prevented. In this article, we solve these problems with the graph neural networks technique. First, each session is represented as a graph rather than a linear sequence structure, based on which a novel Full Graph Neural Network (FGNN) is proposed to learn complicated item dependency. To exploit and incorporate cross-session information in the individual session’s representation learning, we further construct a Broadly Connected Session (BCS) graph to link different sessions and a novel Mask-Readout function to improve session embedding based on the BCS graph. Extensive experiments have been conducted on two e-commerce benchmark datasets, i.e., Yoochoose and Diginetica, and the experimental results demonstrate the superiority of our proposal through comparisons with state-of-the-art session-based recommender models.
- Ting Bai, Jian-Yun Nie, Wayne Xin Zhao, Yutao Zhu, Pan Du, and Ji-Rong Wen. 2018. An attribute-aware neural attentive model for next basket recommendation. In Proceedings of the 41st International ACM SIGIR Conference on Research 8 Development in Information Retrieval (SIGIR’18). 1201--1204.Google Scholar
Digital Library
- Tong Chen, Hongzhi Yin, Hongxu Chen, Rui Yan, Quoc Viet Hung Nguyen, and Xue Li. 2019. AIR: Attentional intention-aware recommender systems. In Proceedings of the 35th IEEE International Conference on Data Engineering (ICDE’19). 304--315.Google ScholarCross Ref
- Tong Chen, Hongzhi Yin, Quoc Viet Hung Nguyen, Wen-Chih Peng, Xue Li, and Xiaofang Zhou. 2020. Sequence-aware factorization machines for temporal predictive analytics. In Proceedings of the 36th IEEE International Conference on Data Engineering (ICDE’20).Google ScholarCross Ref
- Wanyu Chen, Fei Cai, Honghui Chen, and Maarten de Rijke. 2019. Joint neural collaborative filtering for recommender systems. ACM Trans. Info. Syst. 37, 4 (2019), 39:1–39:30.Google ScholarDigital Library
- Junyoung Chung, Caglar Gulcehre, Kyunghyun Cho, and Yoshua Bengio. 2014. Empirical evaluation of gated recurrent neural networks on sequence modeling. In Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems Workshop on Deep Learning (NIPS’14).Google Scholar
- Justin Gilmer, Samuel S. Schoenholz, Patrick F. Riley, Oriol Vinyals, and George E. Dahl. 2017. Neural message passing for quantum chemistry. In Proceedings of the 34th International Conference on Machine Learning (ICML’17). 1263--1272.Google Scholar
- Marco Gori, Gabriele Monfardini, and Franco Scarselli. 2005. A new model for learning in graph domains. In Proceedings of the IEEE International Joint Conference on Neural Networks. Vol. 2. IEEE, 729--734.Google ScholarCross Ref
- Aditya Grover and Jure Leskovec. 2016. node2vec: Scalable feature learning for networks. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery 8 Data Mining (KDD’16). 855--864.Google ScholarDigital Library
- Xinyu Guan, Zhiyong Cheng, Xiangnan He, Yongfeng Zhang, Zhibo Zhu, Qinke Peng, and Tat-Seng Chua. 2019. Attentive aspect modeling for review-aware recommendation. ACM Trans. Info. Syst. 37, 3 (2019), 28:1--28:27.Google ScholarDigital Library
- Lei Guo, Hongzhi Yin, Qinyong Wang, Tong Chen, Alexander Zhou, and Nguyen Quoc Viet Hung. 2019. Streaming session-based recommendation. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery 8 Data Mining (KDD’19). ACM.Google ScholarDigital Library
- William L. Hamilton, Zhitao Ying, and Jure Leskovec. 2017. Inductive representation learning on large graphs. In Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems (NIPS’17). 1025--1035.Google Scholar
- Xiangnan He, Xiaoyu Du, Xiang Wang, Feng Tian, Jinhui Tang, and Tat-Seng Chua. 2018. Outer product-based neural collaborative filtering. In Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI’18). 2227--2233.Google ScholarDigital Library
- Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. 2017. Neural collaborative filtering. In Proceedings of the 26th International Conference on World Wide Web (WWW’17). 173--182.Google ScholarDigital Library
- 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 (CIKM’18). 843--852.Google ScholarDigital Library
- Balázs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, and Domonkos Tikk. 2016. Session-based recommendations with recurrent neural networks. In Proceedings of the 4th International Conference on Learning Representations (ICLR’16).Google Scholar
- Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long short-term memory. Neural Comput. 9, 8 (1997), 1735--1780.Google ScholarDigital Library
- Liang Hu, Qingkui Chen, Haiyan Zhao, Songlei Jian, Longbing Cao, and Jian Cao. 2018. Neural cross-session filtering: Next-item prediction under intra- and inter-session context. IEEE Intell. Syst. 33, 6 (2018), 57--67.Google ScholarDigital Library
- Hyunwoo Hwangbo and Yangsok Kim. 2019. Session-based recommender system for sustainable digital marketing. Sustainability 11, 12 (2019), 3336.Google ScholarCross Ref
- Dietmar Jannach and Malte Ludewig. 2017. When recurrent neural networks meet the neighborhood for session-based recommendation. In Proceedings of the 11nth ACM Conference on Recommender Systems (RecSys’17). 306--310.Google ScholarDigital Library
- Thomas N. Kipf and Max Welling. 2017. Semi-supervised classification with graph convolutional networks. In Proceedings of the 5th International Conference on Learning Representations (ICLR’17).Google Scholar
- Yehuda Koren, Robert M. Bell, and Chris Volinsky. 2009. Matrix factorization techniques for recommender systems. IEEE Comput. 42, 8 (2009), 30--37.Google ScholarDigital Library
- Jingjing Li, Mengmeng Jing, Ke Lu, Lei Zhu, Yang Yang, and Zi Huang. 2019. From zero-shot learning to cold-start recommendation. In Proceedings of the 33rd AAAI Conference on Artificial Intelligence (AAAI’19). 4189--4196.Google ScholarCross Ref
- Jingjing Li, Ke Lu, Zi Huang, and Heng Tao Shen. 2019. On both cold-start and long-tail recommendation with social data. IEEE Trans. Knowl. Data Eng. (2019). DOI:10.1109/TKDE.2019.2924656Google 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 ACM Conference on Information and Knowledge Management (CIKM’17). 1419--1428.Google ScholarDigital Library
- Yujia Li, Chenjie Gu, Thomas Dullien, Oriol Vinyals, and Pushmeet Kohli. 2019. Graph matching networks for learning the similarity of graph structured objects. In Proceedings of the 36th International Conference on Machine Learning (ICML’19). 3835--3845.Google Scholar
- Yujia Li, Daniel Tarlow, Marc Brockschmidt, and Richard S. Zemel. 2016. Gated graph sequence neural networks. In Proceedings of the 4th International Conference on Learning Representations (ICLR’16).Google Scholar
- Zhi Li, Hongke Zhao, Qi Liu, Zhenya Huang, Tao Mei, and Enhong Chen. 2018. Learning from history and present: Next-item recommendation via discriminatively exploiting user behaviors. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery 8 Data Mining (KDD’18). 1734--1743.Google ScholarDigital Library
- Qiao Liu, Yifu Zeng, Refuoe Mokhosi, and Haibin Zhang. 2018. STAMP: Short-term attention/memory priority model for session-based recommendation. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery 8 Data Mining (KDD’18). 1831--1839.Google ScholarDigital Library
- Tomas Mikolov, Ilya Sutskever, Kai Chen, Gregory S. Corrado, and Jeffrey Dean. 2013. Distributed representations of words and phrases and their compositionality. In Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems (NIPS’13).3111--3119.Google ScholarDigital Library
- Vinod Nair and Geoffrey E. Hinton. 2010. Rectified linear units improve restricted boltzmann machines. In Proceedings of the 27th International Conference on Machine Learning (ICML’10). 807--814.Google ScholarDigital Library
- Michael J. Pazzani and Daniel Billsus. 2007. Content-based recommendation systems. In The Adaptive Web, Methods, and Strategies of Web Personalization. Springer, 325--341.Google Scholar
- Bryan Perozzi, Rami Al-Rfou, and Steven Skiena. 2014. DeepWalk: Online learning of social representations. In Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery 8 Data Mining (KDD’14). 701--710.Google ScholarDigital Library
- Ruihong Qiu, Jingjing Li, Zi Huang, and Hongzhi Yin. 2019. Rethinking the item order in session-based recommendation with graph neural networks. In Proceedings of the ACM Conference on Information and Knowledge Management, (CIKM’19).Google ScholarDigital Library
- Massimo Quadrana, Alexandros Karatzoglou, Balázs Hidasi, and Paolo Cremonesi. 2017. Personalizing session-based recommendations with hierarchical recurrent neural networks. In Proceedings of the 11th ACM Conference on Recommender Systems (RecSys’17). 130--137.Google ScholarDigital Library
- Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2009. BPR: Bayesian personalized ranking from implicit feedback. In Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence (UAI’09). 452--461.Google ScholarDigital Library
- Steffen Rendle, Christoph Freudenthaler, and Lars Schmidt-Thieme. 2010. Factorizing personalized Markov chains for next-basket recommendation. In Proceedings of the 19th International Conference on World Wide Web (WWW’10). 811--820.Google ScholarDigital Library
- Badrul Munir Sarwar, George Karypis, Joseph A. Konstan, and John Riedl. 2001. Item-based collaborative filtering recommendation algorithms. In Proceedings of the 10th International World Wide Web Conference (WWW’10). 285--295.Google ScholarDigital Library
- Andrew M. Saxe, James L. McClelland, and Surya Ganguli. 2014. Exact solutions to the nonlinear dynamics of learning in deep linear neural networks. In Proceedings of the 2nd International Conference on Learning Representations (ICLR’14).Google Scholar
- Franco Scarselli, Marco Gori, Ah Chung Tsoi, Markus Hagenbuchner, and Gabriele Monfardini. 2009. The graph neural network model. IEEE Trans. Neural Netw. 20, 1 (2009), 61--80.Google ScholarDigital Library
- J. Ben Schafer, Dan Frankowski, Jonathan L. Herlocker, and Shilad Sen. 2007. Collaborative filtering recommender systems. In The Adaptive Web, Methods, and Strategies of Web Personalization. Springer, 291--324.Google Scholar
- Guy Shani, Ronen I. Brafman, and David Heckerman. 2002. An MDP-based recommender system. In Proceedings of the 18th Conference in Uncertainty in Artificial Intelligence (UAI’02). 453--460.Google Scholar
- Ke Sun, Tieyun Qian, Tong Chen, Yile Liang, Quoc Viet Hung Nguyen, and Hongzhi Yin. 2020. Where to go next: Modeling long and short term user preferences for point-of-interest recommendation. In Proceedings of the 34th AAAI Conference on Artificial Intelligence (AAAI’20).Google Scholar
- Ke Sun, Tieyun Qian, Hongzhi Yin, Tong Chen, Yiqi Chen, and Ling Chen. 2019. What can history tell us? Identifying relevant sessions for next-item recommendation. In Proceedings of the ACM Conference on Information and Knowledge Management (CIKM’19).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 (DLRS@RecSys’16). 17--22.Google ScholarDigital Library
- Jian Tang, Meng Qu, Mingzhe Wang, Ming Zhang, Jun Yan, and Qiaozhu Mei. 2015. LINE: Large-scale information network embedding. In Proceedings of the 24th International Conference on World Wide Web (WWW’15). 1067--1077.Google ScholarDigital Library
- Trinh Xuan Tuan and Tu Minh Phuong. 2017. 3D convolutional networks for session-based recommendation with content features. In Proceedings of the 11th ACM Conference on Recommender Systems (RecSys’17). 138--146.Google ScholarDigital Library
- Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems (NIPS’17). 6000--6010.Google Scholar
- Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio. 2018. Graph attention networks. In Proceedings of the 6th International Conference on Learning Representations (ICLR’18).Google Scholar
- Oriol Vinyals, Samy Bengio, and Manjunath Kudlur. 2016. Order matters: Sequence to sequence for sets. In Proceedings of the 4th International Conference on Learning Representations (ICLR’16).Google Scholar
- Hongwei Wang, Fuzheng Zhang, Jialin Wang, Miao Zhao, Wenjie Li, Xing Xie, and Minyi Guo. 2019. Exploring high-order user preference on the knowledge graph for recommender systems. ACM Trans. Info. Syst. 37, 3 (2019), 32:1--32:26.Google ScholarDigital Library
- Pengfei Wang, Jiafeng Guo, Yanyan Lan, Jun Xu, Shengxian Wan, and Xueqi Cheng. 2015. Learning hierarchical representation model for NextBasket recommendation. In Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’15). 403--412.Google ScholarDigital Library
- Shoujin Wang, Liang Hu, Longbing Cao, Xiaoshui Huang, Defu Lian, and Wei Liu. 2018. Attention-based transactional context embedding for next-item recommendation. In Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI’18). 2532--2539.Google Scholar
- Weiqing Wang, Hongzhi Yin, Xingzhong Du, Quoc Viet Hung Nguyen, and Xiaofang Zhou. 2018. TPM: A temporal personalized model for spatial item recommendation. ACM Trans. Intell. Syst. Technol. 9, 6 (2018), 61:1--61:25.Google ScholarDigital Library
- Weiqing Wang, Hongzhi Yin, Shazia Wasim Sadiq, Ling Chen, Min Xie, and Xiaofang Zhou. 2016. SPORE: A sequential personalized spatial item recommender system. In Proceedings of the 32nd IEEE International Conference on Data Engineering (ICDE’16). 954--965.Google ScholarCross Ref
- Chen Wu and Ming Yan. 2017. Session-aware information embedding for e-commerce product recommendation. In Proceedings of the ACM Conference on Information and Knowledge Management (CIKM’17). 2379--2382.Google ScholarDigital Library
- Shu Wu, Yuyuan Tang, Yanqiao Zhu, Liang Wang, Xing Xie, and Tieniu Tan. 2019. Session-based recommendation with graph neural networks. In Proceedings of the 33rd AAAI Conference on Artificial Intelligence (AAAI’19).Google ScholarCross Ref
- Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka. 2019. How powerful are graph neural networks? In Proceedings of the 5th International Conference on Learning Representations (ICLR’19).Google Scholar
- Zhitao Ying, Jiaxuan You, Christopher Morris, Xiang Ren, William L. Hamilton, and Jure Leskovec. 2018. Hierarchical graph representation learning with differentiable pooling. In Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems (NeurIPS’18). 4805--4815.Google Scholar
- Feng Yu, Qiang Liu, Shu Wu, Liang Wang, and Tieniu Tan. 2016. A dynamic recurrent model for next basket recommendation. In Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval (SIGIR’16). 729--732.Google ScholarDigital Library
- Muhan Zhang, Zhicheng Cui, Marion Neumann, and Yixin Chen. 2018. An end-to-end deep learning architecture for graph classification. In Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI’18). 4438--4445.Google Scholar
- Andrew Zimdars, David Maxwell Chickering, and Christopher Meek. 2001. Using temporal data for making recommendations. In Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence (UAI’01). 580--588.Google ScholarDigital Library
Index Terms
- Exploiting Cross-session Information for Session-based Recommendation with Graph Neural Networks
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