Abstract
Session-based recommendation (SBR) aims to predict the next-interacted item based on an anonymous user behavior sequence (session). The main challenge is how to decipher the user intent with limited interactions. Recent progress regards the combination of consecutive items in the session as intent. However, these methods, which merely depend on the session, ignore the fact that such limited interaction within the session may not entirely express user intent. Therefore, it constrains the expression of diverse user intent without considering the candidate items to be predicted, which can be regarded as target intent, leading to a sub-optimal inference of user behavior. To solve the problem, we propose a novel Intent Alignment Network for session-based recommendation (IAN), which models intent from both session and target perspectives. Specifically, we propose that session-level intent is explicitly formed by weighted aggregation of successive items, whereas target-level intent is composed of interacted and undiscovered items that are compatible. Based on it, we devise an intent alignment mechanism to ensure consistency between these two types of intent and obtain mutual intent representation. Finally, a gated mechanism is used to fuse mutual intent and target intent to generate session representation for prediction. Experimental results on three real-world datasets exhibit that IAN achieves state-of-the-art performance.
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Acknowledgments
We would like to thank the anonymous reviewers for their valuable discussions and constructive feedback. This work was supported by the National Key R&D Program of China (2022YFB4300603), the National Natural Science Foundation of China (U22B2061), Sichuan Science and Technology Program (2023YFG0151), Natural Science Foundation of Sichuan, China (project No. 2024NSFSC0496), the project of China Railway 15th Bureau Group Co., Ltd. (Grant No. 2023B20), and the Development of a Big Data-based Platform for Analyzing the Coupling Relationship of Strip Production Processes Project (211129).
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Dai, T., Liu, Q., Xie, Y., Zeng, Y., Hou, R., Gan, Y. (2024). Session Target Pair: User Intent Perceiving Networks for Session-Based Recommendation. In: Bifet, A., Davis, J., Krilavičius, T., Kull, M., Ntoutsi, E., Žliobaitė, I. (eds) Machine Learning and Knowledge Discovery in Databases. Research Track. ECML PKDD 2024. Lecture Notes in Computer Science(), vol 14941. Springer, Cham. https://doi.org/10.1007/978-3-031-70341-6_16
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DOI: https://doi.org/10.1007/978-3-031-70341-6_16
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