  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.
