Semantic Retrieval Augmented Contrastive Learning for Sequential Recommendation

Understand

Contrastive learning has shown effectiveness in improving sequential recommendation models.

• However, existing methods still face challenges in generating high-quality contrastive pairs: they either rely on random perturbations that corrupt user preference patterns or depend on sparse collaborative data that generates unreliable contrastive pairs.
• Furthermore, existing approaches typically require predefined selection rules that impose strong assumptions, limiting the model's ability to autonomously learn optimal contrastive pairs.
• To address these limitations, we propose a novel approach named Semantic Retrieval Augmented Contrastive Learning (SRA-CL).