用反思与迭代提升推荐系统推理能力，显著改善推荐效果。
R4ec: A Reasoning, Reflection, and Refinement Framework for Recommendation Systems
- 引入思考-反思-优化三阶段框架，模拟慢思考过程
- 在真实广告平台上线后，收入提升2.2%
- 适合需要高精度推理的推荐场景
将大语言模型用于推荐系统已成为热门方向，但现有方法多依赖基础提示技术获取知识，类似快速直觉式思维（System-1），对推理路径中的错误极为敏感。为此，本文提出R⁴ec框架，将推荐系统升级为弱化版慢思考模型（System-2）。该框架包含两个模块：执行推理的演员模型和评估响应、提供反馈的反思模型。演员模型根据反馈迭代优化自身输出，形成循环反思与修正机制，使LLM实现更谨慎的推理。最终，经优化的知识被融入推荐主干进行预测。我们在Amazon-Book和MovieLens-1M数据集上进行了广泛实验，验证了R⁴ec的有效性。此外，在大规模在线广告平台部署后，系统收入提升了2.2%。我们还研究了演员模型与反思模型的扩展特性。
原文摘要 · Abstract (English)
Harnessing Large Language Models (LLMs) for recommendation systems has emerged as a prominent avenue, drawing substantial research interest. However, existing approaches primarily involve basic prompt techniques for knowledge acquisition, which resemble System-1 thinking. This makes these methods highly sensitive to errors in the reasoning path, where even a small mistake can lead to an incorrect inference. To this end, in this paper, we propose $R^{4}$ec, a reasoning, reflection and refinement framework that evolves the recommendation system into a weak System-2 model. Specifically, we introduce two models: an actor model that engages in reasoning, and a reflection model that judges these responses and provides valuable feedback. Then the actor model will refine its response based on the feedback, ultimately leading to improved responses. We employ an iterative reflection and refinement process, enabling LLMs to facilitate slow and deliberate System-2-like thinking. Ultimately, the final refined knowledge will be incorporated into a recommendation backbone for prediction. We conduct extensive experiments on Amazon-Book and MovieLens-1M datasets to demonstrate the superiority of $R^{4}$ec. We also deploy $R^{4}$ec on a large scale online advertising platform, showing 2.2\% increase of revenue. Furthermore, we investigate the scaling properties of the actor model and reflection model.
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