Triplet Contrastive Learning with Learnable Sequence Augmentation for Sequential Recommendation
Wei Wang, Yujie Lin, Moyan Zhang, Hongyu Lu, Jianli Zhao, Jie Sun, Xianye Ben, Pengjie Ren, Yujun Li · 2025
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.