Exploiting cross‐session information for knowledge‐aware session‐based recommendation via graph attention networks

Abstract

Session-based recommendation (SBR) aims to predict the next item based on anonymous behavior session, which has become increasingly essential in various online services. Prior efforts mainly focus on modeling user preference based on the current session. Although some of them have been proven effective, they fail to address two main challenges in SBR. First, SBR suffers more from the problem of data sparsity due to the very limited user–item interactions, and hence it cannot sufficiently capture complicated item dependency relationships. Second, most of the user-item interaction sequences may be with noisy preference signals due to the uncertainty of user's behaviors, and it is difficult to distill high-quality item for recommendation. In this study, we propose a novel SBR model that exploits Cross-session information for Knowledge-aware Session-based Recommendation (CKSR) to address these two issues. Specifically, cross-session graph and knowledge graph are combined to model a cross-session knowledge graph, based on which a knowledge-aware attention mechanism is performed to capture the complicated transition pattern among interacted items. Each session is then represented as the composition of the global preference and the current interest of that session. Moreover, we leverage the similar sessions for the target session to establish a similar session referral circle and apply an influence coupler to judge the significance of different session referrals. An attentive network is designed to distill session preferences from its unique session referral circle. It dynamically extracts high-quality item from noisy session. Experiments on two benchmark data sets demonstrate that CKSR outperforms the state-of-the-art methods consistently.