  Self-supervised learning (SSL) has recently achieved great success in mining
the user-item interactions for collaborative filtering. As a major paradigm,
contrastive learning (CL) based SSL helps address data sparsity in Web
platforms by contrasting the embeddings between raw and augmented data.
However, existing CL-based methods mostly focus on contrasting in a batch-wise
way, failing to exploit potential regularity in the feature dimension. This
leads to redundant solutions during the representation learning of users and
items. In this work, we investigate how to employ both batch-wise CL (BCL) and
feature-wise CL (FCL) for recommendation. We theoretically analyze the relation
between BCL and FCL, and find that combining BCL and FCL helps eliminate
redundant solutions but never misses an optimal solution. We propose a dual
contrastive learning recommendation framework -- RecDCL. In RecDCL, the FCL
objective is designed to eliminate redundant solutions on user-item positive
pairs and to optimize the uniform distributions within users and items using a
polynomial kernel for driving the representations to be orthogonal; The BCL
objective is utilized to generate contrastive embeddings on output vectors for
enhancing the robustness of the representations. Extensive experiments on four
widely-used benchmarks and one industry dataset demonstrate that RecDCL can
consistently outperform the state-of-the-art GNNs-based and SSL-based models
(with an improvement of up to 5.65\% in terms of Recall@20). The source code is
publicly available (https://github.com/THUDM/RecDCL).
