Abstract
Session-based recommendation is a challenging problem due to the limited session data. In the real scene, there are two insights in sessions: (1) Hierarchical intents: the implicit hierarchy in user preference is a common phenomenon, since users usually click a specific item with a general intent. (2) The influence of the current interest: the items that users click in order have sequence dependencies, and the next item is affected by the current operation. However, recent approaches are all inherently flat and neglect the hierarchical intents. Besides, they neglect the truly related subsequence for modeling the current interest. This can lead to inaccurate user intents, and fail when the user’s next click tendency falls into the more general intent. In this paper, we propose a method modeling from both Hierarchical Intents and Selective Sequential Interests (HISSI). Methodologically, we design a general intent abstractor to extract the common features and transmit general intents through the hierarchy to form fine-to-coarse grained intents. In addition, a selector-GRU is proposed to model the user’s subsequence behavior that is related to the last click without noises. Extensive experiments on three real-world datasets verify our model’s effectiveness.
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This work was supported by Beijing Municipal Science and Technology Project Z201100001820003.
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Zhang, M., Guo, C., Jin, J., Pan, M., Fang, J. (2021). Modeling Hierarchical Intents and Selective Current Interest for Session-Based Recommendation. In: Karlapalem, K., et al. Advances in Knowledge Discovery and Data Mining. PAKDD 2021. Lecture Notes in Computer Science(), vol 12713. Springer, Cham. https://doi.org/10.1007/978-3-030-75765-6_33
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DOI: https://doi.org/10.1007/978-3-030-75765-6_33
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