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.
- [1] . 2020. Recurrent convolutional networks for session-based recommendations. Neurocomputing 411 (2020), 247–258.Google ScholarCross Ref
- [2] . 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.Google ScholarDigital Library
- [3] . 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.Google ScholarCross Ref
- [4] . 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.Google Scholar
- [5] . 2003. Latent Dirichlet allocation. J. Mach. Learn. Res. 3 (2003), 993–1022.Google ScholarCross Ref
- [6] . 1994. On Gibbs sampling for state space models. Biometrika 81, 3 (1994), 541–553.Google ScholarCross Ref
- [7] . 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
- [8] . 2019. AIR: Attentional intention-aware recommender systems. In Proceedings of the IEEE 35th International Conference on Data Engineering (ICDE). 304–315.
DOI: Google ScholarCross Ref - [9] . 2021. Intention adaptive graph neural network for category-aware session-based recommendation. arXiv preprint arXiv:2112.15352 (2021).Google Scholar
- [10] . 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.Google ScholarCross Ref
- [11] . 2019. Hypergraph neural networks. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 33. 3558–3565.Google ScholarDigital Library
- [12] . 2019. Streaming session-based recommendation. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 1569–1577.Google ScholarDigital Library
- [13] . 2019. NISER: Normalized item and session representations to handle popularity bias. arXiv preprint arXiv:1909.04276 (2019).Google Scholar
- [14] . 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
- [15] . 2015. Session-based recommendations with recurrent neural networks. arXiv preprint arXiv:1511.06939 (2015).Google Scholar
- [16] . 1999. Probabilistic latent semantic indexing. In Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. 50–57.Google ScholarDigital Library
- [17] . 2019. Session-based recommender system for sustainable digital marketing. Sustainability 11, 12 (2019), 3336.Google ScholarCross Ref
- [18] . 2017. When recurrent neural networks meet the neighborhood for session-based recommendation. In Proceedings of the 11th ACM Conference on Recommender Systems. 306–310.Google ScholarDigital Library
- [19] . 2018. Self-attentive sequential recommendation. In Proceedings of the IEEE International Conference on Data Mining (ICDM). IEEE, 197–206.Google ScholarCross Ref
- [20] . 2021. Intention-aware sequential recommendation with structured intent transition. IEEE Trans. Knowl. Data Eng. (2021).
DOI: Google ScholarCross Ref - [21] . 2017. Neural attentive session-based recommendation. In Proceedings of the ACM on Conference on Information and Knowledge Management. 1419–1428.Google ScholarDigital Library
- [22] . 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.Google ScholarDigital Library
- [23] . 2016. Context-aware sequential recommendation. In Proceedings of the IEEE 16th International Conference on Data Mining (ICDM). IEEE, 1053–1058.Google ScholarCross Ref
- [24] . 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.Google ScholarDigital Library
- [25] . 2022. Mixed information flow for cross-domain sequential recommendations. ACM Trans. Knowl. Discov. Data 16, 4 (2022), 1–32.Google ScholarDigital Library
- [26] . 2000. Text classification from labeled and unlabeled documents using EM. Mach. Learn. 39, 2 (2000), 103–134.Google ScholarDigital Library
- [27] . 2022. Collaborative graph learning for session-based recommendation. ACM Trans. Inf. Syst. 40, 4 (2022), 1–26.Google ScholarDigital Library
- [28] . 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.Google ScholarDigital Library
- [29] . 2022. LDGC-SR: Integrating long-range dependencies and global context information for session-based recommendation. Knowl.-based Syst. 248 (2022), 108894.Google ScholarDigital Library
- [30] . 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.Google ScholarDigital Library
- [31] . 2017. Personalizing session-based recommendations with hierarchical recurrent neural networks. In Proceedings of the 11th ACM Conference on Recommender Systems. 130–137.Google ScholarDigital Library
- [32] . 2010. Factorizing personalized Markov chains for next-basket recommendation. In Proceedings of the 19th International Conference on World Wide Web. 811–820.Google ScholarDigital Library
- [33] . 2022. Sequential recommendation with user evolving preference decomposition. arXiv preprint arXiv:2203.16942 (2022).Google Scholar
- [34] . 2022. MBN: Towards multi-behavior sequence modeling for next basket recommendation. ACM Trans. Knowl. Discov. Data 16, 5 (2022), 1–23.Google ScholarDigital Library
- [35] . 2019. Research commentary on recommendations with side information: A survey and research directions. Electron. Commerce Res. Applic. 37 (2019), 100879.Google ScholarDigital Library
- [36] . 2022. CGSNet: Contrastive graph self-attention network for session-based recommendation. Knowl.-based Syst. 251 (2022), 109282.Google ScholarDigital Library
- [37] . 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.Google ScholarDigital Library
- [38] . 2022. Exploiting intra-and inter-session dependencies for session-based recommendations. World Wide Web 25, 1 (2022), 425–443.Google ScholarDigital Library
- [39] . 2021. A survey on session-based recommender systems. ACM Comput. Surv. 54, 7 (2021), 1–38.Google ScholarDigital Library
- [40] . 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.Google ScholarCross Ref
- [41] . 2018. Variational recurrent model for session-based recommendation. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management. 1839–1842.Google ScholarDigital Library
- [42] . 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.Google ScholarDigital Library
- [43] . 2019. Session-based recommendation with graph neural networks. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 33. 346–353.Google ScholarDigital Library
- [44] . 2020. Garg: Anonymous recommendation of point-of-interest in mobile networks by graph convolution network. Data Sci. Eng. 5, 4 (2020), 433–447.Google ScholarCross Ref
- [45] . 2013. Personalized next-song recommendation in online karaokes. In Proceedings of the 7th ACM Conference on Recommender Systems. 137–140.Google ScholarDigital Library
- [46] . 2020. Self-supervised hypergraph convolutional networks for session-based recommendation. arXiv preprint arXiv:2012.06852 (2020).Google Scholar
- [47] . 2019. Graph contextualized self-attention network for session-based recommendation. In Proceedings of the International Joint Conference on Artificial Intelligence, Vol. 19. 3940–3946.Google ScholarCross Ref
- [48] . 2013. A biterm topic model for short texts. In Proceedings of the 22nd International Conference on World Wide Web. 1445–1456.Google ScholarDigital Library
- [49] . 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.Google ScholarDigital Library
- [50] . 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.Google ScholarDigital Library
- [51] . 2022. DSGNN: A dynamic and static intentions integrated graph neural network for session-based recommendation. Neurocomputing 468 (2022), 222–232.Google ScholarDigital Library
- [52] . 2011. Comparing Twitter and traditional media using topic models. In Proceedings of the European Conference on Information Retrieval. Springer, 338–349.Google ScholarDigital Library
- [53] . 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.Google ScholarCross Ref
- [54] . 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.Google ScholarDigital Library
- [55] . 2021. Learning a hierarchical intent model for next-item recommendation. ACM Trans. Inf. Syst. 40, 2 (2021), 1–28.Google ScholarDigital Library
Index Terms
- SMONE: A Session-based Recommendation Model Based on Neighbor Sessions with Similar Probabilistic Intentions
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