ITGNN: Item Transition Attentive Graph Neural Network for Session-Based Recommendation

Abstract

In e-commerce scenario, the goal of a session-based recommendation system(SBRS) is to predict next item clicks of users, while the session is anonymous in the process. Graph Neural Network(GNN) has aroused widespread interest in the SBRS system. However, there are two important drawbacks of applying GNN for this task. First, classic GNN approaches rarely consider the time interval between the user clicking on two items as in real-world environment. This time interval is very important for accurately recommending the next user's interest. Secondly, for inter-session information, most GNN-based models consider n-hop neighbor nodes of a single node, which introduces more noise in the learn global information phase.In this paper, we propose a Item Transition Attentive Graph Neural Network model(ITGNN). This model learns how time interval and global frequency of item transition affect a local session. Extensive experiments conducted on two E-commerce datasets demonstrate the model to be more accurate in recommendation task.