Multi-behavior recommendation with SVD Graph Neural Networks

• Published in: Expert systems with applications 2024-09, Vol.249, p.123575, Article 123575
• Main Authors: Fu, Shengxi, Ren, Qianqian, Lv, Xingfeng, Li, Jinbao
• Format: Article
• Language: English
• Subjects: Contrastive learning; Graph Neural Networks; Multi-behavior recommendation; Singular value decomposition
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2024.123575

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. •Proposing a novel contrastive learning paradigm for multi-behavior recommendation.•Solving the data sparsity and noise problem simultaneously.•Adopting SVD method for graph augmentation to enhance model performance.•Experiments on real-world datasets show the superiority of the proposed model.