SRA-CL: Semantic Retrieval Augmented Contrastive Learning for Sequential Recommendation

• URL: http://arxiv.org/abs/2503.04162v4
• Date: Mon, 20 Oct 2025 09:37:16 GMT
• Title: SRA-CL: Semantic Retrieval Augmented Contrastive Learning for Sequential Recommendation
• Authors: Ziqiang Cui, Yunpeng Weng, Xing Tang, Xiaokun Zhang, Shiwei Li, Peiyang Liu, Bowei He, Dugang Liu, Weihong Luo, Xiuqiang He, Chen Ma,
• Abstract summary: We propose a novel approach named Semantic Retrieval Augmented Contrastive Learning (SRA-CL)<n>SRA-CL leverages the semantic understanding and reasoning capabilities of LLMs to generate expressive embeddings that capture user preferences and item characteristics.<n>SRA-CL adopts a plug-and-play design, enabling seamless integration with existing sequential recommendation architectures.
• Score: 23.050104678143935
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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). SRA-CL leverages the semantic understanding and reasoning capabilities of LLMs to generate expressive embeddings that capture both user preferences and item characteristics. These semantic embeddings enable the construction of candidate pools for inter-user and intra-user contrastive learning through semantic-based retrieval. To further enhance the quality of the contrastive samples, we introduce a learnable sample synthesizer that optimizes the contrastive sample generation process during model training. SRA-CL adopts a plug-and-play design, enabling seamless integration with existing sequential recommendation architectures. Extensive experiments on four public datasets demonstrate the effectiveness and model-agnostic nature of our approach.

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