Multi-interest Awareness Model for Session-based Recommendation

Caihong Mu, Jiahuan Chen, Yunlong Chen, Yi Liu

Abstract: Session-based recommendation (SBR) aims to recommend items based on anonymous behavior sequences. Methods based on graph neural networks always ignore two issues. First, they ignore the multiple user interests hidden in sessions. Second, they do not pay much attention to the impact of the over-smoothing phenomenon in graph neural networks. To address these two problems, this paper proposes a novel multi-interest awareness (MIA) model by modeling multiple interests explicitly for SBR. First, we design a multi-interest extraction module to extract user interests hidden in sessions. Specifically, a bilinear mapping matrix models multiple user interests by mapping items in the session to different interest capsules. A gating network allows the session to select different user interests through the gating mechanism. Then, the interest aggregation module uses an interest anchor to aggregate effective user interest information at each layer, so that the over-smoothing problem is alleviated. Experimental results show that our approach is competitive compared to the state-of-the-art models.