Context-aware Session-based Recommendation with Graph Neural Networks

• URL: http://arxiv.org/abs/2310.09593v1
• Date: Sat, 14 Oct 2023 14:29:52 GMT
• Title: Context-aware Session-based Recommendation with Graph Neural Networks
• Authors: Zhihui Zhang, JianXiang Yu, Xiang Li
• Abstract summary: We propose CARES, a novel context-aware session-based recommendation model with graph neural networks. We first construct a multi-relation cross-session graph to connect items according to intra- and cross-session item-level contexts. To encode the variation of user interests, we design personalized item representations.
• Score: 6.825493772727133
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Session-based recommendation (SBR) is a task that aims to predict items based on anonymous sequences of user behaviors in a session. While there are methods that leverage rich context information in sessions for SBR, most of them have the following limitations: 1) they fail to distinguish the item-item edge types when constructing the global graph for exploiting cross-session contexts; 2) they learn a fixed embedding vector for each item, which lacks the flexibility to reflect the variation of user interests across sessions; 3) they generally use the one-hot encoded vector of the target item as the hard label to predict, thus failing to capture the true user preference. To solve these issues, we propose CARES, a novel context-aware session-based recommendation model with graph neural networks, which utilizes different types of contexts in sessions to capture user interests. Specifically, we first construct a multi-relation cross-session graph to connect items according to intra- and cross-session item-level contexts. Further, to encode the variation of user interests, we design personalized item representations. Finally, we employ a label collaboration strategy for generating soft user preference distribution as labels. Experiments on three benchmark datasets demonstrate that CARES consistently outperforms state-of-the-art models in terms of P@20 and MRR@20. Our data and codes are publicly available at https://github.com/brilliantZhang/CARES.

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