用推理增强对比学习与协同奖励强化，提升大模型在序列推荐中的表现
Harmonizing Semantic and Collaborative in LLMs: Reasoning-based Embedding Generator for Sequential Recommendation
- 通过两阶段前向+注意力模块挖掘大模型内部推理能力
- 在三个真实数据集上显著优于现有方法，有效缓解长尾问题
- 适合关注大模型语义理解与协同信号融合的研究者
序列推荐系统（SRS）基于用户交互历史预测下一感兴趣项目，广泛应用但受长尾问题制约。大语言模型（LLMs）具备强大的语义理解与推理能力，可作为嵌入生成器丰富物品语义，但当前方法存在两大缺陷：一是未能充分挖掘模型内在推理能力；二是协同信号多通过监督微调隐式注入，缺乏对协同嵌入对齐的显式引导。本文提出ReaEmb框架，通过潜推理增强对比学习（LRCL）阶段与协同奖励强化学习（CRRL）阶段解决上述问题。LRCL采用双通道前向传播结合额外注意力模块，激发LLM的内部推理能力；CRRL则通过定制强化学习显式将协同信号注入LLM。在三个真实世界数据集上的大量实验表明，ReaEmb在多个SRS模型上均表现出更优效果。为促进复现，代码已公开。
原文摘要 · Abstract (English)
Sequential Recommender Systems (SRS) predict the next item of interest based on users' interaction histories and have been widely deployed, but hindered by long-tail problem. Large Language Models (LLMs), with strong semantic understanding and reasoning capabilities, offer a promising way to enrich item semantics and have recently been used as embedding generators. However, two fundamental gaps remain. First, current LLM-based embedding methods fail to exploit the model's inner reasoning capacity. Second, existing methods often inject collaborative signals implicitly via supervised fine-tuning, lacking explicit guidance for collaborative embedding alignment. In this paper, we introduce ReaEmb, a novel framework that resolves both issues via a Latent Reasoning-enhanced Contrastive Learning (LRCL) stage and a Collaborative Reward Reinforcement Learning (CRRL) stage. LRCL exploits the LLMs' inner reasoning capacity through a two-pass forward process with an additional attention module. CRRL subsequently explicitly injects collaborative signals into the LLM via a tailored reinforcement learning. Extensive experiments on three real-world datasets demonstrate superior effectiveness of ReaEmb across multiple SRS models. To ease reproducibility, we release the code online.
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