Session-based Recommendation with Preference Interaction

Authors

  • Jingyuan He
  • Bailong Yang
  • Yuan Tian

DOI:

https://doi.org/10.54097/jceim.v10i1.5336

Keywords:

Graph neural network, Session-based recommendation, Item-self information

Abstract

Graph neural network have achieved great success in session-based recommendation. Recently, some works have achieved improvement by incorporating income and outcome adjacent matrices to generate global and local preferences, and directly model the two preferences to build session representation. However, firstly, we observe that the income matrix and outcome matrix of a session have no strong relevance, and their concatenation may introduce noise for building two preferences. Secondly, we find the global and local preferences can benefit from each other, and collaborative information from neighborhood sessions may help to improve recommendation performance. Therefore, we propose a session-based recommendation with preference interaction from separate income adjacent matrix and outcome adjacent matrix framework, which includes two parallel modules: An Income Session Representation Encoder (ISE) and an Outcome Session Representation En-coder (OSE). A fusion gating mechanism is introduced to balance the importance of session representations resulting from ISE and OSE. The experimental results show that our model obviously outperforms other state-of-the-art methods on Yoochoose and Diginetica datasets.

References

Song, J., Shen, H., Ou, Z., Zhang, J.,Xiao, T., and Liang, S. 2019. ISLF: Interest Shift and Latent Factors Combination Model for Session-based Recommendation. In Proceedings of the 28th International Joint Conference on Artifical Intelligence, 5765-5771.

Wang, M.; Ren, P.; Mei, L.; Chen, Z.; Ma, J.; and Rijke, M. D. 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.

Wu, S.; Tang, Y.; Zhu, Y.; Wang, L.; Xie, X.; and Tan T. 2019a. Session-Based Recommendation with Graph Neural Networks. In Proceedings of the 33rd AAAI Conference on Artificial Intelligence, 346-353.

Hidasi B, Karatzoglou A, Baltrunas L, et al. Session-based recommendations with recurrent neural networks. In Proceedings of the 4th International Conference on Learning Representation, 2016.

Rendle, S.; Freudenthaler, C.; and Schmidt-Thieme, L. 2010. Factorizing Personalized Markov Chains for Next-Basket Recommendation. In Proceedings of the 19th International Conference on World Wide Web, 811-820.

Davidson, J.; Liebald, B.; Liu, J.; Nandy, P.; Van Vleet, T.; and Gargi, U. 2010. The YouTube Video Recommendation System. In Proceedings of the 4th ACM Conference on Recommender Systems, 293-296.

Hidasi B and Karatzoglou A. Recurrent neural networks with top-k gains for session-based recommendations. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management, 2018: 843–852.

Li J, Ren P, Chen Z, et al. Neural attentive session-based recommendation. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, 2017: 1419-1428.

Yu, F.; Zhu, Y.; Liu, Q.; Wei, S.; Wang, L.; and Tan, T. 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.

Garg, D.; Gupta, P.; Malhotra, P.; Vig, L.; and Shroff, G. 2019. Sequence and Time Aware Neighborhood for Session-Based Recommendation. In Proceedings of the 42rd International ACM SIGIR Conference on Research and Development in Information Retrieval, 1069-1072.

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Published

20-02-2023

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Section

Articles

How to Cite

He, J., Yang, B., & Tian, Y. (2023). Session-based Recommendation with Preference Interaction. Journal of Computing and Electronic Information Management, 10(1), 32-34. https://doi.org/10.54097/jceim.v10i1.5336

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