How to Bridge Graph and Sequence Patterns in Session-Based Recommendation? A Self-Supervised Method

Abstract

Session-based Recommendation aims to reveal the item distribution patterns in anonymous session sequences. Most existing approaches model the distribution patterns by utilizing either sequential or structural information individually to absorb different pattern knowledge, which can only model the distinct one-sided facet of item distribution in sessions, thus leading to suboptimal performance. Self-supervised learning provides a natural solution as a bridge to fill the gap between different learning paradigms in session-based recommendations, which remains unexplored. In this paper, we regard the distinct learning paradigm as an individual channel and then integrate the sequential and graphical channels with a contrastive bridge architecture. We name the novel framework DC-Rec, for Dual Channel Recommendation, to model the comprehensive session characteristics. Extensive experiments conducted on two real-world datasets demonstrate that our model consistently outperforms the state-of-the-art recommendation methods.