Relationship-Enhanced Session-based Recommendation with Graph Neural Networks

Abstract

The session-based recommendation provides accurate recommendations for anonymous users by analyzing their preferences through their behavior data in recent times. Existing session-based recommendation systems methods do not adequately utilize the click orders and click counts of session items. They do not fully capture the dependency between each item in a session. We propose a new model for session-based recommendation, RESR-GNN, to resolve these issues. The main contributions of the paper are as follows: (1) To differentiate the importance of different items, designing the corresponding relationship-enhanced session graph with the session, including click information; (2) Soft attention and multi-layer self-attention mechanisms are used to comprehensively analyse preferences between each item in an entire session. We conducted multiple comparative experiments using two publicly available datasets, and our findings show that RESR-GNN performs better than other baseline models.