RAU: Towards Regularized Alignment and Uniformity for Representation Learning in Recommendation

• URL: http://arxiv.org/abs/2503.18300v1
• Date: Mon, 24 Mar 2025 03:03:21 GMT
• Title: RAU: Towards Regularized Alignment and Uniformity for Representation Learning in Recommendation
• Authors: Xi Wu, Dan Zhang, Chao Zhou, Liangwei Yang, Tianyu Lin, Jibing Gong,
• Abstract summary: We propose Regularized Alignment and Uniformity (RAU) to cope with sparse alignment and uneven uniformity issues.<n>RAU consists of two novel regularization methods for alignment and uniformity to learn better user/item representation.
• Score: 7.193305599721105
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recommender systems (RecSys) have become essential in modern society, driving user engagement and satisfaction across diverse online platforms. Most RecSys focuses on designing a powerful encoder to embed users and items into high-dimensional vector representation space, with loss functions optimizing their representation distributions. Recent studies reveal that directly optimizing key properties of the representation distribution, such as alignment and uniformity, can outperform complex encoder designs. However, existing methods for optimizing critical attributes overlook the impact of dataset sparsity on the model: limited user-item interactions lead to sparse alignment, while excessive interactions result in uneven uniformity, both of which degrade performance. In this paper, we identify the sparse alignment and uneven uniformity issues, and further propose Regularized Alignment and Uniformity (RAU) to cope with these two issues accordingly. RAU consists of two novel regularization methods for alignment and uniformity to learn better user/item representation. 1) Center-strengthened alignment further aligns the average in-batch user/item representation to provide an enhanced alignment signal and further minimize the disparity between user and item representation. 2) Low-variance-guided uniformity minimizes the variance of pairwise distances along with uniformity, which provides extra guidance to a more stabilized uniformity increase during training. We conducted extensive experiments on three real-world datasets, and the proposed RAU resulted in significant performance improvements compared to current state-of-the-art CF methods, which confirms the advantages of the two proposed regularization methods.

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