Triplet Contrastive Learning with Learnable Sequence Augmentation for Sequential Recommendation
Year: 2025
Type: conference-paper
Abstract: The quality of augmented data directly affects the performance of contrastive learning. Low-quality augmentation offers limited benefits for model optimization. Existing contrastive learning-based sequential recommendation works primarily utilize heuristic data augmentation methods, which often exhibit excessive randomness and struggle to generate positive samples that align with users' true intentions.
Authors: Wei Wang, Yujie Lin, Moyan Zhang, Hongyu Lu, Jianli Zhao, Jie Sun, Xianye Ben, Pengjie Ren, Yujun Li
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
Awards: G2782232053, G5502878137, G6283261777
OpenAlex ID: W4412377909