Cited By
View all- Yang YSun JAn G(2024)Exploring multi-dimensional interests for session-based recommendationMultimedia Systems10.1007/s00530-024-01437-230:5Online publication date: 13-Aug-2024
SMONE: A Session-based Recommendation Model Based on Neighbor Sessions with Similar Probabilistic Intentions
Authors: 
Bohan Jia, Jian Cao, Shiyou Qian, Nengjun Zhu, Xin Dong, Liang Zhang, Lei Cheng, Linjian MoAuthors Info & Claims
Article No.: 111, Pages 1 - 22
Abstract
A session-based recommendation system (SRS) tries to predict the next possible choice of anonymous users. In recent years, graph neural network (GNN) models have been successfully applied to SRSs and have achieved great success. Using GNN models in SRSs, each session graph is processed successively to obtain the embedding of the node (i.e, each action on an item), which is then imported into the prediction module to generate recommendation results. However, solely depending on the session graph to obtain the node embeddings is not sufficient because each session only involves a few items. Therefore, neighbor sessions have been used to extend the session graph to learn more informative node representations. In this paper, we introduce a Session-based recommendation MOdel based on Neighbor sessions with similar probabilistic int Entions(SMONE). SMONE models the intentions behind sessions in a probabilistic way and retrieves the neighbor sessions with similar intentions. After the neighbor sessions are found, the target session and its neighbor sessions are modeled as a hypyergraph to learn the contextualized embeddings, which are combined with item embeddings through GNN to produce the final item recommendations. Experiments on real-world datasets prove the effectiveness and superiority of SMONE.
References
[1]
Ngo Xuan Bach, Dang Hoang Long, and Tu Minh Phuong. 2020. Recurrent convolutional networks for session-based recommendations. Neurocomputing 411 (2020), 247–258.
[2]
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 & Development in Information Retrieval. 1201–1204.
[3]
Bhagyashree Vyankatrao Barde and Anant Madhavrao Bainwad. 2017. An overview of topic modeling methods and tools. In Proceedings of the International Conference on Intelligent Computing and Control Systems (ICICCS). IEEE, 745–750.
[4]
Marco Bertini, Andrea Ferracani, Riccardo Papucci, and Alberto Del Bimbo. 2020. Keeping up with the influencers: Improving user recommendation in Instagram using visual content. In Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization. 29–34.
[5]
David M. Blei, Andrew Y. Ng, and Michael I. Jordan. 2003. Latent Dirichlet allocation. J. Mach. Learn. Res. 3 (2003), 993–1022.
[6]
Chris K. Carter and Robert Kohn. 1994. On Gibbs sampling for state space models. Biometrika 81, 3 (1994), 541–553.
[7]
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.
[8]
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 IEEE 35th International Conference on Data Engineering (ICDE). 304–315. DOI:
[9]
Chuan Cui, Qi Shen, Shixuan Zhu, Yitong Pang, Yiming Zhang, Zhenwei Dong, and Zhihua Wei. 2021. Intention adaptive graph neural network for category-aware session-based recommendation. arXiv preprint arXiv:2112.15352 (2021).
[10]
Ramazan Esmeli, Mohamed Bader-El-Den, and Alaa Mohasseb. 2019. Context and short term user intention aware hybrid session based recommendation system. In Proceedings of the IEEE International Symposium on INnovations in Intelligent SysTems and Applications (INISTA). IEEE, 1–6.
[11]
Yifan Feng, Haoxuan You, Zizhao Zhang, Rongrong Ji, and Yue Gao. 2019. Hypergraph neural networks. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 33. 3558–3565.
[12]
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 & Data Mining. 1569–1577.
[13]
Priyanka Gupta, Diksha Garg, Pankaj Malhotra, Lovekesh Vig, and Gautam Shroff. 2019. NISER: Normalized item and session representations to handle popularity bias. arXiv preprint arXiv:1909.04276 (2019).
[14]
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.
[15]
Balázs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, and Domonkos Tikk. 2015. Session-based recommendations with recurrent neural networks. arXiv preprint arXiv:1511.06939 (2015).
[16]
Thomas Hofmann. 1999. Probabilistic latent semantic indexing. In Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. 50–57.
[17]
Hyunwoo Hwangbo and Yangsok Kim. 2019. Session-based recommender system for sustainable digital marketing. Sustainability 11, 12 (2019), 3336.
[18]
Dietmar Jannach and Malte Ludewig. 2017. When recurrent neural networks meet the neighborhood for session-based recommendation. In Proceedings of the 11th ACM Conference on Recommender Systems. 306–310.
[19]
Wang-Cheng Kang and Julian McAuley. 2018. Self-attentive sequential recommendation. In Proceedings of the IEEE International Conference on Data Mining (ICDM). IEEE, 197–206.
[20]
Haoyang Li, Xin Wang, Ziwei Zhang, Jianxin Ma, Peng Cui, and Wenwu Zhu. 2021. Intention-aware sequential recommendation with structured intent transition. IEEE Trans. Knowl. Data Eng. (2021). DOI:
[21]
Jing Li, Pengjie Ren, Zhumin Chen, Zhaochun Ren, Tao Lian, and Jun Ma. 2017. Neural attentive session-based recommendation. In Proceedings of the ACM on Conference on Information and Knowledge Management. 1419–1428.
[22]
Lin Liu, Li Wang, and Tao Lian. 2021. Discovering proper neighbors to improve session-based recommendation. In Proceedings of the Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Springer, 353–369.
[23]
Qiang Liu, Shu Wu, Diyi Wang, Zhaokang Li, and Liang Wang. 2016. Context-aware sequential recommendation. In Proceedings of the IEEE 16th International Conference on Data Mining (ICDM). IEEE, 1053–1058.
[24]
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 & Data Mining. 1831–1839.
[25]
Muyang Ma, Pengjie Ren, Zhumin Chen, Zhaochun Ren, Lifan Zhao, Peiyu Liu, Jun Ma, and Maarten de Rijke. 2022. Mixed information flow for cross-domain sequential recommendations. ACM Trans. Knowl. Discov. Data 16, 4 (2022), 1–32.
[26]
Kamal Nigam, Andrew Kachites McCallum, Sebastian Thrun, and Tom Mitchell. 2000. Text classification from labeled and unlabeled documents using EM. Mach. Learn. 39, 2 (2000), 103–134.
[27]
Zhiqiang Pan, Fei Cai, Wanyu Chen, Chonghao Chen, and Honghui Chen. 2022. Collaborative graph learning for session-based recommendation. ACM Trans. Inf. Syst. 40, 4 (2022), 1–26.
[28]
Zhiqiang Pan, Fei Cai, Yanxiang Ling, and Maarten de Rijke. 2020. An intent-guided collaborative machine for session-based recommendation. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. 1833–1836.
[29]
Nan Qiu, BoYu Gao, Huawei Tu, Feiran Huang, Quanlong Guan, and Weiqi Luo. 2022. LDGC-SR: Integrating long-range dependencies and global context information for session-based recommendation. Knowl.-based Syst. 248 (2022), 108894.
[30]
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 28th ACM International Conference on Information and Knowledge Management. 579–588.
[31]
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. 130–137.
[32]
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. 811–820.
[33]
Weiqi Shao, Xu Chen, Long Xia, Jiashu Zhao, and Dawei Yin. 2022. Sequential recommendation with user evolving preference decomposition. arXiv preprint arXiv:2203.16942 (2022).
[34]
Yanyan Shen, Baoyuan Ou, and Ranzhen Li. 2022. MBN: Towards multi-behavior sequence modeling for next basket recommendation. ACM Trans. Knowl. Discov. Data 16, 5 (2022), 1–23.
[35]
Zhu Sun, Qing Guo, Jie Yang, Hui Fang, Guibing Guo, Jie Zhang, and Robin Burke. 2019. Research commentary on recommendations with side information: A survey and research directions. Electron. Commerce Res. Applic. 37 (2019), 100879.
[36]
Fuyun Wang, Xuequan Lu, and Lei Lyu. 2022. CGSNet: Contrastive graph self-attention network for session-based recommendation. Knowl.-based Syst. 251 (2022), 109282.
[37]
Meirui Wang, Pengjie Ren, Lei Mei, Zhumin Chen, Jun Ma, and Maarten de Rijke. 2019. A collaborative session-based recommendation approach with parallel memory modules. In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval. 345–354.
[38]
Nan Wang, Shoujin Wang, Yan Wang, Quan Z. Sheng, and Mehmet A. Orgun. 2022. Exploiting intra-and inter-session dependencies for session-based recommendations. World Wide Web 25, 1 (2022), 425–443.
[39]
Shoujin Wang, Longbing Cao, Yan Wang, Quan Z. Sheng, Mehmet A. Orgun, and Defu Lian. 2021. A survey on session-based recommender systems. ACM Comput. Surv. 54, 7 (2021), 1–38.
[40]
Shoujin Wang, Liang Hu, and Longbing Cao. 2017. Perceiving the next choice with comprehensive transaction embeddings for online recommendation. In Proceedings of the Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Springer, 285–302.
[41]
Zhitao Wang, Chengyao Chen, Ke Zhang, Yu Lei, and Wenjie Li. 2018. Variational recurrent model for session-based recommendation. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management. 1839–1842.
[42]
Ziyang Wang, Wei Wei, Gao Cong, Xiao-Li Li, Xian-Ling Mao, and Minghui Qiu. 2020. Global context enhanced graph neural networks for session-based recommendation. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. 169–178.
[43]
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 AAAI Conference on Artificial Intelligence, Vol. 33. 346–353.
[44]
Shiwen Wu, Yuanxing Zhang, Chengliang Gao, Kaigui Bian, and Bin Cui. 2020. Garg: Anonymous recommendation of point-of-interest in mobile networks by graph convolution network. Data Sci. Eng. 5, 4 (2020), 433–447.
[45]
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.
[46]
Xin Xia, Hongzhi Yin, Junliang Yu, Qinyong Wang, Lizhen Cui, and Xiangliang Zhang. 2020. Self-supervised hypergraph convolutional networks for session-based recommendation. arXiv preprint arXiv:2012.06852 (2020).
[47]
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 Proceedings of the International Joint Conference on Artificial Intelligence, Vol. 19. 3940–3946.
[48]
Xiaohui Yan, Jiafeng Guo, Yanyan Lan, and Xueqi Cheng. 2013. A biterm topic model for short texts. In Proceedings of the 22nd International Conference on World Wide Web. 1445–1456.
[49]
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. 729–732.
[50]
Feng Yu, Yanqiao Zhu, Qiang Liu, Shu Wu, Liang Wang, and Tieniu Tan. 2020. TAGNN: Target attentive graph neural networks for session-based recommendation. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. 1921–1924.
[51]
Chunkai Zhang, Quan Liu, and Zeyu Zhang. 2022. DSGNN: A dynamic and static intentions integrated graph neural network for session-based recommendation. Neurocomputing 468 (2022), 222–232.
[52]
Wayne Xin Zhao, Jing Jiang, Jianshu Weng, Jing He, Ee-Peng Lim, Hongfei Yan, and Xiaoming Li. 2011. Comparing Twitter and traditional media using topic models. In Proceedings of the European Conference on Information Retrieval. Springer, 338–349.
[53]
Yujia Zheng, Siyi Liu, Zekun Li, and Shu Wu. 2020. DGTN: Dual-channel graph transition network for session-based recommendation. In Proceedings of the International Conference on Data Mining Workshops (ICDMW). IEEE, 236–242.
[54]
Nengjun Zhu, Jian Cao, Yanchi Liu, Yang Yang, Haochao Ying, and Hui Xiong. 2020. Sequential modeling of hierarchical user intention and preference for next-item recommendation. In Proceedings of the 13th International Conference on Web Search and Data Mining. 807–815.
[55]
Nengjun Zhu, Jian Cao, Xinjiang Lu, and Hui Xiong. 2021. Learning a hierarchical intent model for next-item recommendation. ACM Trans. Inf. Syst. 40, 2 (2021), 1–28.
Index Terms
- SMONE: A Session-based Recommendation Model Based on Neighbor Sessions with Similar Probabilistic Intentions
Recommendations
A social recommendation method based on an adaptive neighbor selection mechanism
An adaptive neighbor selection mechanism is proposed for recommender systems.The proposed method incorporates social information into recommendation process.The quality of predicted ratings is evaluated using a reliability measure.A new confidence model ...
Long-tail Session-based Recommendation
RecSys '20: Proceedings of the 14th ACM Conference on Recommender SystemsSession-based recommendation focuses on the prediction of user actions based on anonymous sessions and is a necessary method in the lack of user historical data. However, none of the existing session-based recommendation methods explicitly takes the ...
Context-aware item attraction model for session-based recommendation
Highlights- Propose an item attraction model for session-based recommendation.
- Convert sequence-structured sessions into local and global undirected graphs.
- Mine item adjacency relevance in session graphs for users’ general interests.
- Mine ...
AbstractSession-based recommendation uses existing items in users’ interaction sessions to predict the next items with which users will interact. The existing items in sessions usually have different degrees of relevance with each other, and this item ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
September 2023
348 pages
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 12 May 2023
Online AM: 09 March 2023
Accepted: 27 February 2023
Revised: 15 February 2023
Received: 30 June 2022
Published in TKDD Volume 17, Issue 8
Check for updates
Author Tags
Qualifiers
- Research-article
Funding Sources
- China National Science Foundation
- Program of Technology Innovation of the Science and Technology Commission of Shanghai Municipality
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- View Citations1Total Citations
- 546Total Downloads
- Downloads (Last 12 months)201
- Downloads (Last 6 weeks)16
Reflects downloads up to 11 Dec 2024
Other Metrics
Citations
Cited By
View all- Yang YSun JAn G(2024)Exploring multi-dimensional interests for session-based recommendationMultimedia Systems10.1007/s00530-024-01437-230:5Online publication date: 13-Aug-2024
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in