RecDCL: Dual Contrastive Learning for Recommendation

Dan Zhang, Yangliao Geng, Wenwen Gong, Zhongang Qi, Zhiyu Chen, Xing Tang, Ying Shan, Yuxiao Dong, Jie Tang

Keywords: Recommender Systems, Self-supervised Learning, Feature-wise Contrastive Learning, Batch-wise Contrastive Learning

Abstract: Self-supervised recommendation (SSR) has achieved great success in mining the potential interacted behaviors for collaborative filtering in recent years. As a major branch, Contrastive Learning (CL) based SSR conquers data sparsity in Web platforms by contrasting the embedding between raw data and augmented data. However, existing CL-based SSR methods mostly focus on contrasting in a batch-wise way, failing to exploit potential regularity in the feature-wise dimension, leading to redundant solutions during the representation learning process of users (items) from Websites. Furthermore, the joint benefits of utilizing both Batch-wise CL (BCL) and Feature-wise CL (FCL) for recommendations remain underexplored. To address these issues, we investigate the relationship of objectives between BCL and FCL. Our study suggests a cooperative benefit of employing both methods, as evidenced from theoretical and experimental perspectives. Based on these insights, we propose a dual CL method for recommendation, referred to as RecDCL. RecDCL first eliminates redundant solutions on user-item positive pairs in a feature-wise manner. It then optimizes the uniform distributions within users and items using a polynomial kernel from an FCL perspective. Finally, it generates contrastive embedding on output vectors in a batch-wise objective. We conduct experiments on four widely-used benchmarks and an industrial dataset. The results consistently demonstrate that the proposed RecDCL outperforms the state-of-the-art GNNs-based and SSL-based models (with up to a 5.65\% improvement in terms of Recall@20), thereby confirming the effectiveness of the joint-wise objective. All source codes used in this paper are publicly available\footnote{\url{https://anonymous.4open.science/r/RecDCL-FC33/}}.

Track: User Modeling and Recommendation

Submission Guidelines Scope: Yes

Submission Guidelines Blind: Yes

Submission Guidelines Format: Yes

Submission Guidelines Limit: Yes

Submission Guidelines Authorship: Yes

Student Author: Yes

Submission Number: 1288