Abstract
Next-item recommendation involves predicting the next item of interest of a given user from their past behavior. Users tend to browse and purchase various items on e-commerce websites according to their varied interests and needs, as reflected in their purchasing history. Most existing next-item recommendation methods aim at extracting the main point of interest in each browsing session and encapsulate it in a single representation. However, past behavior sequences reflect the multiple interests of a single user, which cannot be captured by methods that focus on single-interest contexts. Indeed, multiple interests cannot be captured in a single representation, and doing so results in missing information. Therefore, we propose a model with a multi-interest structure for capturing the various interests of users from their behavior sequence. Moreover, we adopted a method based on a graph neural network to construct interest graphs based on the historical and current behavior sequences of users. These graphs can capture complex item transition patterns related to different interests. In experiments, the proposed method outperforms state-of-the-art session-based recommendation systems on three real-world datasets, achieving 4% improvement of Recall over the SOTAs on Jdata dataset.
- [1] . 2016. Improving the accuracy of latent-space-based recommender systems by introducing a cut-off criterion. In Proceedings of the EnCHIReS@ EICS. 44–53.Google Scholar
- [2] . 2020. Controllable multi-interest framework for recommendation. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2942–2951.Google ScholarDigital Library
- [3] . 2018. Sequential recommendation with user memory networks. In Proceedings of the 11th ACM International Conference on Web Search and Data Mining. 108–116.Google ScholarDigital Library
- [4] . 2012. Cross-domain recommender systems: A survey of the state of the art. In Proceedings of the Spanish Conference on Information Retrieval. sn, 1–12.Google Scholar
- [5] . 2020. Session-based recommendation with hierarchical leaping networks. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. 1705–1708.Google ScholarDigital Library
- [6] . 2016. Fast matrix factorization for online recommendation with implicit feedback. In Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval. 549–558.Google ScholarDigital Library
- [7] . 2016. Session-based recommendations with recurrent neural networks. In Proceedings of the International Conference on Learning Representations. http://arxiv.org/abs/1511.06939.Google Scholar
- [8] . 2018. Self-attentive sequential recommendation. In Proceedings of the 2018 IEEE International Conference on Data Mining. IEEE, 197–206.Google ScholarCross Ref
- [9] . 2009. Matrix factorization techniques for recommender systems. Computer 42, 8 (2009), 30–37.Google ScholarDigital Library
- [10] . 2019. Multi-interest network with dynamic routing for recommendation at tmall. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management. 2615–2623.Google ScholarDigital Library
- [11] . 2017. Neural attentive session-based recommendation. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management. 1419–1428.Google ScholarDigital Library
- [12] . 2020. DDTCDR: Deep dual transfer cross domain recommendation. In Proceedings of the 13th International Conference on Web Search and Data Mining. 331–339.Google ScholarDigital Library
- [13] Yujia Li, Richard Zemel, Marc Brockschmidt, and Daniel Tarlow. 2016. Gated Graph Sequence Neural Networks. In Proceedings of ICLR16. arXiv:1511.05493. Retrieved from https://arxiv.org/abs/1511.05493.Google Scholar
- [14] . 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
- [15] . 2018. A recommender system based on collaborative filtering using ontology and dimensionality reduction techniques. Expert Systems with Applications 92 (2018), 507–520.Google ScholarDigital Library
- [16] . 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
- [17] . 2017. Personalizing session-based recommendations with hierarchical recurrent neural networks. In Proceedings of the 11th ACM Conference on Recommender Systems. ACM, 130–137.Google ScholarDigital Library
- [18] . 2019. Repeatnet: A repeat aware neural recommendation machine for session-based recommendation. In Proceedings of the AAAI Conference on Artificial Intelligence. 4806–4813.Google ScholarDigital Library
- [19] . 2009. BPR: Bayesian personalized ranking from implicit feedback. In Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence. 452–461. arXiv:1205.2618. Retrieved from https://arxiv.org/abs/1205.2618.Google Scholar
- [20] . 2010. Factorizing personalized markov chains for next-basket recommendation. In Proceedings of the 19th International Conference on World Wide Web. ACM, 811–820.Google ScholarDigital Library
- [21] . 2017. Dynamic routing between capsules. In Advances in Neural Information Processing Systems 30 (2017). arXiv:1710.09829. Retrieved from https://arxiv.org/abs/1710.09829.Google Scholar
- [22] . 2016. A semantic approach to remove incoherent items from a user profile and improve the accuracy of a recommender system. Journal of Intelligent Information Systems 47, 1 (2016), 111–134.Google ScholarDigital Library
- [23] . 2001. Item-based collaborative filtering recommendation algorithms. In Proceedings of the 10th International Conference on World Wide Web. 285–295.Google ScholarDigital Library
- [24] . 2008. The graph neural network model. IEEE Transactions on Neural Networks 20, 1 (2008), 61–80.Google ScholarDigital Library
- [25] . 2007. Collaborative filtering recommender systems. In Proceedings of the Adaptive Web. Springer, 291–324.Google ScholarDigital Library
- [26] . 2005. An MDP-based recommender system. Journal of Machine Learning Research 6, 9 (2005), 1265–1295.Google ScholarDigital Library
- [27] . 2019. How good your recommender system is? A survey on evaluations in recommendation. International Journal of Machine Learning and Cybernetics 10, 5 (2019), 813–831.Google ScholarDigital Library
- [28] . 2021. Next-item recommendations in short sessions. In Fifteenth ACM Conference on Recommender Systems. 282–291.Google ScholarDigital Library
- [29] . 2016. Improved recurrent neural networks for session-based recommendations. In Proceedings of the 1st Workshop on Deep Learning for Recommender Systems. 17–22.Google ScholarDigital Library
- [30] . 2018. Personalized top-n sequential recommendation via convolutional sequence embedding. In Proceedings of the 11th ACM International Conference on Web Search and Data Mining. ACM, 565–573.Google ScholarDigital Library
- [31] . 2017. Attention is all you need. In Proceedings of the Advances in Neural Information Processing Systems. 5998–6008.Google Scholar
- [32] . 2020. PA-GGAN: Session-based recommendation with position-aware gated graph attention network. In Proceedings of the 2020 IEEE International Conference on Multimedia and Expo. IEEE, 1–6.Google ScholarCross Ref
- [33] . 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
- [34] . 2021. A survey on session-based recommender systems. ACM Computing Surveys 54, 7 (2021), 1–38.Google ScholarDigital Library
- [35] . 2018. Attention-based transactional context embedding for next-item recommendation. In Proceedings of the 32nd AAAI Conference on Artificial Intelligence.Google ScholarCross Ref
- [36] . 2020. Intention nets: Psychology-inspired user choice behavior modeling for next-basket prediction. In Proceedings of the AAAI Conference on Artificial Intelligence. 6259–6266.Google ScholarCross Ref
- [37] . 2020. Intention2Basket: A neural intention-driven approach for dynamic next-basket planning. In Proceedings of the 29th International Joint Conference on Artificial Intelligence. 2333–2339.Google ScholarCross Ref
- [38] . 2019. Modeling multi-purpose sessions for next-item recommendations via mixture-channel purpose routing networks. In Proceedings of the IJCAI. 3771–3777.Google ScholarCross Ref
- [39] . 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
- [40] . 2019. Session-based recommendation with graph neural networks. In Proceedings of the AAAI Conference on Artificial Intelligence. 33. 346–353.Google ScholarDigital Library
- [41] . 2019. Graph contextualized self-attention network for session-based recommendation. In Proceedings of the IJCAI. 3940–3946.Google ScholarCross Ref
- [42] . 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. arXiv:2005.02844. Retrieved from https://arxiv.org/abs/2005.02844.Google Scholar
- [43] . 2019. A simple convolutional generative network for next item recommendation. In Proceedings of the 12th ACM International Conference on Web Search and Data Mining. 582–590.Google ScholarDigital Library
- [44] . 2020. Learning personalized itemset mapping for cross-domain recommendation. In Proceedings of the 29th International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence Organization, 2561–2567.Google ScholarCross Ref
- [45] . 2021. Learning a hierarchical intent model for next-item recommendation. ACM Transactions on Information Systems 40, 2 (2021), 1–28.Google ScholarDigital Library
Index Terms
- Modeling Cross-session Information with Multi-interest Graph Neural Networks for the Next-item Recommendation
Recommendations
Heterogeneous Global Graph Neural Networks for Personalized Session-based Recommendation
WSDM '22: Proceedings of the Fifteenth ACM International Conference on Web Search and Data MiningPredicting the next interaction of a short-term interaction session is a challenging task in session-based recommendation. Almost all existing works rely on item transition patterns, and neglect user historical sessions while modeling user preference, ...
Exploiting Group Information for Personalized Recommendation with Graph Neural Networks
Personalized recommendation has become more and more important for users to quickly find relevant items. The key issue of the recommender system is how to model user preferences. Previous work mostly employed user historical data to learn users’ ...
Modelling Local and Global Dependencies for Next-Item Recommendations
Web Information Systems Engineering – WISE 2020AbstractSession-based recommender systems (SBRSs) aim at predicting the next item by modelling the complex dependencies within and across sessions. Most of the existing SBRSs make recommendations only based on local dependencies (i.e., the dependencies ...
Comments