Modeling Complex Dependencies for Session-based Recommendations via Graph Neural Networks

TL;DR

This paper introduces RI-GNN, a graph neural network model for session-based recommendations that leverages review-based topic information to better capture true item dependencies, overcoming adjacency assumptions.

Contribution

The paper proposes a novel review-refined inter-item graph neural network (RI-GNN) that incorporates review-derived topic information to improve dependency modeling in session-based recommendations.

Findings

RI-GNN outperforms state-of-the-art methods on real-world datasets.

Using review information reduces false and missing dependencies.

The approach enhances item representation learning for better recommendations.

Abstract

Session-based recommendations (SBRs) capture items' dependencies from the sessions to recommend the next item. In recent years, Graph neural networks (GNN) based SBRs have become the mainstream of SBRs benefited from the superiority of GNN in modeling complex dependencies. Based on a strong assumption of adjacent dependency, any two adjacent items in a session are necessarily dependent in most GNN-based SBRs. However, we argue that due to the uncertainty and complexity of user behaviors, adjacency does not necessarily indicate dependency. However, the above assumptions do not always hold in actual recommendation scenarios, so it can easily lead to two drawbacks: (1) false dependencies occur in the session because there are adjacent but not really dependent items, and (2) the missing of true dependencies occur in the session because there are non-adjacent but actually dependent items. These drawbacks significantly affect item representation learning, degrading the downstream recommendation performance. To address these deficiencies, we propose a novel review-refined inter-item graph neural network (RI-GNN), which utilizes topic information extracted from the reviews of items to improve dependencies between items. Experiments on two public real-world datasets demonstrate that RI-GNN outperforms SOTA methods.