Adaptive Adjustment Graph Augmentation and Representation Structures for Recommendation Model

Collaborative Filtering (CF) is an effective recommendation method that predicts user preferences by learning the representations of users and items. A recent study on CF has improved representation quality and enhanced recommendation performance from the perspective of hypersphere alignment and uniformity. The present study promotes alignment to increase the similarity between the representations of interacting users and items and enhances uniformity, resulting in a more evenly distributed representation of users and items within the hyper sphere. However, the use of only supervised data for alignment and uniform representation optimization ignores issues such as behavioral noise, data sparsity, and differences in popularity, which inevitably damage the generalization performance and structural characteristics of the representation. To address these issues, a more accurate adaptive alignment and uniform recommendation model is proposed. The data is modeled as a bipartite graph of user-item interaction and a Graph Neural Network (GNN) is applied to learn user and item representations. The model performs self-supervised contrastive learning on user and project representations to capture additional graph structure patterns unrelated to the supervised data. During optimization, the alignment and uniformity optimization objectives are adaptively adjusted based on popularity, thereby achieving a more generalized alignment and uniformity. Extensive experiments are conducted on three real-world datasets, and the results demonstrate the superiority and robustness of the proposed model over the baseline models.