FiCoRec: Fine-Grained Contrastive Learning with Dual Aggregation for Sequential Recommendation

Abstract

Sequential recommendation methods integrated with contrastive learning have been proven effective in addressing the data sparsity issue. However, most contrastive learning schemes directly perform random data augmentation on original sequences, which struggles to capture fine-grained features in users’ historical interaction sequences. Meanwhile, these augmentation methods lack semantic consistency. Additionally, most approaches employ a single aggregation strategy for user representation, making it difficult to comprehensively characterize user preferences. To tackle these issues, we propose a Fine-Grained Contrastive Learning with Dual Aggregation approach for Sequential Recommendation (FiCoRec). Specifically, we design four tailored data augmentation methods on user embedding sequences to ensure semantic consistency and adaptability, and construct rich self-supervised signals, thereby enabling fine-grained contrastive learning. Furthermore, we design a Dual Aggregation module to capture the Tail Aggregation features and Global Aggregation features of sequences, which facilitates the comprehensive learning of users’ short-term key interests and long-term global preferences. Extensive experiments conducted on four public datasets demonstrate that FiCoRec achieves superior performance compared with existing baseline models, with up to 45.93% increase in Mean Reciprocal Rank (MRR). The code is available in https://github.com/Y-point/FiCoRec.