用语言模型增强推荐系统，让冷启动更准。
RecLM: Recommendation Instruction Tuning
- 将大语言模型与协同过滤结合，用指令微调提升理解力
- 在稀疏数据和零样本场景下显著优于传统方法
- 可无缝接入现有推荐系统，适合做个性化推荐研究
现代推荐系统旨在通过用户历史交互深入理解其复杂偏好。尽管基于图神经网络（GNN）的深度协同过滤方法在捕捉用户-项目关系方面表现优异，但在数据稀疏或零样本场景下效果受限，主要源于基于ID的嵌入函数的局限性。为此，我们提出一种模型无关的推荐指令微调范式，将大语言模型与协同过滤无缝融合。所提出的RecLM通过精心设计的强化学习奖励函数，实现语言模型的自我增强，有效提升对用户偏好多样性的捕捉能力。全面评估表明，该方法在多种设置下均表现显著优越，且与当前最优推荐系统具备即插即用兼容性，带来明显性能提升。RecLM框架代码已公开：https://github.com/HKUDS/RecLM。
原文摘要 · Abstract (English)
Modern recommender systems aim to deeply understand users' complex preferences through their past interactions. While deep collaborative filtering approaches using Graph Neural Networks (GNNs) excel at capturing user-item relationships, their effectiveness is limited when handling sparse data or zero-shot scenarios, primarily due to constraints in ID-based embedding functions. To address these challenges, we propose a model-agnostic recommendation instruction-tuning paradigm that seamlessly integrates large language models with collaborative filtering. Our proposed $\underline{Rec}$ommendation $\underline{L}$anguage $\underline{M}$odel (RecLM) enhances the capture of user preference diversity through a carefully designed reinforcement learning reward function that facilitates self-augmentation of language models. Comprehensive evaluations demonstrate significant advantages of our approach across various settings, and its plug-and-play compatibility with state-of-the-art recommender systems results in notable performance enhancements. The implementation of our RecLM framework is publicly available at: https://github.com/HKUDS/RecLM.
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