Enhanced graph neural network for session-based recommendation

Abstract

A key problem of session-based recommendation is to predict items of interest based on anonymous user’s current interaction session. To improve the prediction accuracy, existing methods mainly adopted two paradigms, i.e., sequence pattern and co-occurrence pattern. Though decent results were achieved, these methods still could not better simulate the real intention of users, or consider the complex pattern transformation between interacted items. To address these limitations, we propose a novel framework called Enhanced Graph Neural Network (E-GNN) for session-based recommendation, where the interaction sequence of all anonymous users is modeled as a Weighted Global Item Graph (WGIG) and the current interaction session of a target user is modeled as a Local Session Graph (LSG). Specifically, the WGIG is first constructed to produce a weight matrix effectively revealing the connection patterns of all intereacted items. The LSG is then constructed to explicitly model the initial interaction patterns of items in the target user’s current session. Finally, the weighting matrix and LSG are input into our proposed fusion algorithm, and the E-GNN representing the target user’s real interaction pattern is the output. The main contributions of this paper are: (1) Multiple interaction patterns are explicitly modelled in one interaction session graph; and (2) A new fusion algorithm is proposed to reconstruct current session graph by integrating the extracted weights representing the directivity of interacted items from the WGIG. A large number of comparative experiments are conducted on two public datasets, and the results show that E-GNN has distinct advantages over other baseline models.