Multi-behavior Recommendation with SVD Graph Neural Networks

TL;DR

This paper introduces MB-SVD, a GNN-based multi-behavior recommendation model that leverages SVD graphs and simplified contrastive learning to improve recommendation accuracy and robustness against noise.

Contribution

It proposes a novel multi-behavior recommendation model using SVD graphs and a simplified contrastive learning framework within GNNs.

Findings

MB-SVD outperforms existing methods on real-world datasets.

The model effectively integrates multi-behavior user preferences.

Enhanced robustness against noise interference in recommendations.

Abstract

Graph Neural Networks (GNNs) have been extensively employed in the field of recommendation systems, offering users personalized recommendations and yielding remarkable outcomes. Recently, GNNs incorporating contrastive learning have demonstrated promising performance in handling the sparse data problem of recommendation systems. However, existing contrastive learning methods still have limitations in resisting noise interference, especially for multi-behavior recommendation. To mitigate the aforementioned issues, this paper proposes a GNN-based multi-behavior recommendation model called MB-SVD that utilizes Singular Value Decomposition (SVD) graphs to enhance model performance. In particular, MB-SVD considers user preferences across different behaviors, improving recommendation effectiveness. First, MB-SVD integrates the representation of users and items under different behaviors with learnable weight scores, which efficiently considers the influence of different behaviors. Then, MB-SVD generates augmented graph representation with global collaborative relations. Next, we simplify the contrastive learning framework by directly contrasting original representation with the enhanced representation using the InfoNCE loss. Through extensive experimentation, the remarkable performance of our proposed MB-SVD approach in multi-behavior recommendation endeavors across diverse real-world datasets is exhibited.