2020/12/10 by Ziyang Wang, Wei Wei, Wang, Ziyang +7
Computer Science · Decision Sciences · #Advanced Bandit Algorithms Research #FOS: Computer and information sciences #Image and Video Quality Assessment #Information Retrieval (cs.IR) #Recommender Systems and Techniques
paper · pdf · doi:10.48550/arxiv.2012.05422
openalex publication_date 2020/12/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.