让大模型像人一样思考推荐理由，提升推荐准确性与可解释性。
Reason4Rec: Deliberative User Preference Alignment of Large Language Models for Recommendation
- 引入分步推理机制，让模型逐步分析用户偏好
- 在三个真实数据集上显著提升推荐准确率与推理质量
- 适合需要可解释推荐的电商、内容平台场景
将大语言模型（LLMs）对齐推荐任务是推荐领域的新范式，整体表现良好。然而，现有对齐方式仅让模型直接生成用户反馈，缺乏深度思考，在复杂场景下表现不佳。为此，我们提出新的“深思熟虑推荐”任务，将显式推理用户偏好作为额外对齐目标。为此，我们设计了基于分步专家协作的推理增强推荐框架，利用逐层展开的用户反馈文本，结合定制化训练策略，强化模型推理能力。在三个真实世界数据集上的大量实验表明，该任务设定合理，所提框架有效提升了预测精度与推理质量。代码已开源：https://github.com/Peter-Fy/Reason4Rec。
原文摘要 · Abstract (English)
Aligning Large Language Models (LLMs) with recommendation tasks represents an emerging paradigm in recommendation domain, exhibiting promising performance overall. However, these aligned recommendation LLMs often struggle with complex scenarios due to limitations in the current alignment task formulation, which optimizes LLMs to directly generate user feedback without deliberation. To develop more reliable recommendation LLMs, we introduce a new \textit{Deliberative Recommendation} task, which enforces explicit reasoning about user preferences as an additional alignment objective. To address this task, we propose a \textit{Reasoning-powered Recommender} framework designed to enhance reasoning capabilities by leveraging verbalized user feedback in a step-wise manner. Specifically, this framework employs collaborative step-wise experts alongside specifically crafted expert-wise training strategies. Extensive experiments conducted on three real-world datasets demonstrate the rationality of the deliberative task formulation and the effectiveness of the proposed framework in improving both prediction accuracy and reasoning quality. Our implementation is publicly available on GitHub: https://github.com/Peter-Fy/Reason4Rec.
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