Exploiting Group-Level Behavior Pattern for Session-Based Recommendation
Ziyang Wang, Wei Wei, Shanshan Feng, Xian-Ling Mao, Minghui Qiu, Dangyang Chen, Rui Fang
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, the existing studies focus on the instance-level session learning, while neglecting the group-level users’ preferences (e.g., the common preferences of group users in repeat consumption). To this end, we propose a novel <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</b> epeat-aware <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</b> eural <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</b> echanism for <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</b> ession-based <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</b> ecommendation (RNMSR). In RNMSR, we propose to learn the user preference from two levels: (i) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">instance-level</i> , which employs GNNs on a similarity-based item-pairwise session graph to capture the users’ preference in instance-level. (ii) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">group-level</i> , which converts sessions into group-level behavior patterns to model the group-level users’ preferences. In RNMSR, we combine instance-level and group-level user preference to model the repeat consumption of users, i.e., whether users take repeated consumption and which items are preferred by users. Extensive experiments are conducted on three real-world datasets, i.e., Diginetica, Yoochoose, and Nowplaying, demonstrating that the proposed method consistently achieves state-of-the-art performance in all the tests.