Exploiting Cross-Session Information for Session-based Recommendation with Graph Neural Networks

Abstract

Different from the traditional recommender system, the session-based recommender system introduces the concept of the session, i.e., a sequence of interactions between a user and multiple items within a period, to preserve the user's recent interest. The existing work on the session-based recommender system mainly relies on mining sequential patterns within individual sessions, which are not expressive enough to capture more complicated dependency relationships among items. In addition, it does not consider the cross-session information due to the anonymity of the session data, where the linkage between different sessions is prevented. In this paper, we solve these problems with the graph neural networks technique. First, each session is represented as a graph rather than a linear sequence structure, based on which a novel Full Graph Neural Network (FGNN) is proposed to learn complicated item dependency. To exploit and incorporate cross-session information in the individual session's representation learning, we further construct a Broadly Connected Session (BCS) graph to link different sessions and a novel Mask-Readout function to improve session embedding based on the BCS graph. Extensive experiments have been conducted on two e-commerce benchmark datasets, i.e., Yoochoose and Diginetica, and the experimental results demonstrate the superiority of our proposal through comparisons with state-of-the-art session-based recommender models.

arXiv commentarXiv admin note: substantial text overlap with arXiv:1911.11942

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• Contrastive Learning for Representation Degeneration Problem in Sequential Recommendation
• Lightweight Self-Attentive Sequential Recommendation
• Time Interval-enhanced Graph Neural Network for Shared-account Cross-domain Sequential Recommendation
• Reinforcement Learning-enhanced Shared-account Cross-domain Sequential Recommendation
• HMSG: Heterogeneous Graph Neural Network based on Metapath Subgraph Learning
• Exploiting Positional Information for Session-based Recommendation
• CausalRec: Causal Inference for Visual Debiasing in Visually-Aware Recommendation
• Contemporary Recommendation Systems on Big Data and Their Applications: A Survey
• M2TRec: Metadata-aware Multi-task Transformer for Large-scale and Cold-start free Session-based Recommendations
• Long-Tail Session-based Recommendation from Calibration
• Recommending on graphs: a comprehensive review from a data perspective
• A Survey on GNN-based Link Prediction: Techniques, Applications, and Challenges