RecDCL: Dual Contrastive Learning for Recommendation

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 at \url{https://github.com/THUDM/RecDCL}}.

BatchSampler: Sampling Mini-Batches for Contrastive Learning in Vision, Language, and Graphs

In-Batch contrastive learning is a state-of-the-art self-supervised method that brings semantically-similar instances close while pushing dissimilar instances apart within a mini-batch. Its key to success is the negative sharing strategy, in which every instance serves as a negative for the others within the mini-batch. Recent studies aim to improve performance by sampling hard negatives \textit{within the current mini-batch}, whose quality is bounded by the mini-batch itself. In this work, we propose to improve contrastive learning by sampling mini-batches from the input data. We present BatchSampler\footnote{The code is available at \url{https://github.com/THUDM/BatchSampler}} to sample mini-batches of hard-to-distinguish (i.e., hard and true negatives to each other) instances. To make each mini-batch have fewer false negatives, we design the proximity graph of randomly-selected instances. To form the mini-batch, we leverage random walk with restart on the proximity graph to help sample hard-to-distinguish instances. BatchSampler is a simple and general technique that can be directly plugged into existing contrastive learning models in vision, language, and graphs. Extensive experiments on datasets of three modalities show that BatchSampler can consistently improve the performance of powerful contrastive models, as shown by significant improvements of SimCLR on ImageNet-100, SimCSE on STS (language), and GraphCL and MVGRL on graph datasets.