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arXiv · 2012.05422

Exploiting Group-level Behavior Pattern forSession-based Recommendation

Ziyang Wang·Wei Wei·Xian-Ling Mao·Xiao-Li Li·Shanshan Feng
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Abstract

Session-based recommendation (SBR) is a challenging task, which aims to predict users' future interests based on anonymous behavior sequences. Existing methods leverage powerful representation learning approaches to encode sessions into a low-dimensional space. However, despite such achievements, all the existing studies focus on the instance-level session learning, while neglecting the group-level users' preference, which is significant to model the users' behavior. To this end, we propose a novel Repeat-aware Neural Mechanism for Session-based Recommendation (RNMSR). In RNMSR, we propose to learn the user preference from both instance-level and group-level, respectively: (i) instance-level, which employs GNNs on a similarity-based item-pairwise session graph to capture the users' preference in instance-level. (ii) group-level, which converts sessions into group-level behavior patterns to model the group-level users' preference. In RNMSR, we combine instance-level user preference and group-level user preference to model the repeat consumption of users, \ie whether users take repeated consumption and which items are preferred by users. Extensive experiments are conducted on three real-world datasets, \ie Diginetica, Yoochoose, and Nowplaying, demonstrating that the proposed method consistently achieves state-of-the-art performance in all the tests.

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Ziyang Wang, Wei Wei, Xian-Ling Mao, Xiao-Li Li, Shanshan Feng. 2020-12-10. Exploiting Group-level Behavior Pattern forSession-based Recommendation. https://arxiv.org/abs/2012.05422

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