Prototypical Graph Contrastive Learning for Recommendation

Abstract

Data sparsity caused by limited interactions makes it challenging for recommendation to accurately capture user preferences. Contrastive learning effectively alleviates this issue by enriching embedding information through the learning of diverse contrastive views. The effectiveness of contrastive learning in uncovering users’ and items’ latent preferences largely depends on the construction of data augmentation strategies. Structure and feature perturbations are commonly used augmentation strategies in graph-based contrastive learning. Since graph structure augmentation is time consuming and can disrupt interaction information, this paper proposes a novel feature augmentation contrastive learning method. This approach leverages preference prototypes to guide user and item embeddings in acquiring augmented information. By generating refined prototypes for each user and item based on existing prototypes to better approximate true preferences, it effectively alleviates the over-smoothing issue within similar preferences. To balance feature augmentation, a prototype filtering network is employed to control the flow of prototype information, ensuring consistency among different embeddings. Compared with existing prototype-based methods, ProtoRec achieves maximum gains of up to 16.8% and 20.0% in recall@k and NDCG@k on the Yelp2018, Douban-Book, and Amazon-Book datasets.

Keywords

• contrastive learning
• data augmentation
• recommendation
• self-supervised learning