用分步推理优化推荐智能体，更懂用户细微偏好。
AgenticRec: A Recommendation-Oriented Agentic Framework with Progressive Tool-Integrated Reasoning Optimization
- 将推荐建模为工具驱动的渐进式推理过程。
- 在隐式反馈下提升推荐精准度，显著优于基线模型。
- 适合构建高精度个性化推荐系统的研究者与工程师。
基于大语言模型的推荐智能体为个性化推荐提供了新范式，但现有方法常因工具推理路径与推荐反馈不一致，难以区分用户细粒度偏好。为此，我们提出 AgenticRec，一种面向推荐任务的智能体框架，将推荐建模为在推荐导向工具集上的工具集成推理过程。在此框架基础上，设计了两阶段训练策略：第一阶段引入推荐导向轨迹激活，在隐式反馈下优化智能体推荐能力；第二阶段通过自举硬样本的双向偏好推理，逐步精炼偏好边界。理论分析与大量实验验证了该方法的有效性。代码已开源。
原文摘要 · Abstract (English)
Recommender agents built on Large Language Models offer a promising paradigm for personalized recommendation. However, existing agents typically suffer from a misalignment between their tool-integrated reasoning trajectories and recommendation feedback, limiting their ability to distinguish fine-grained user preferences. To address these challenges, we propose AgenticRec, an agentic recommendation framework that formulates recommendation as a tool-integrated reasoning process over a recommendation-oriented tool suite. Built upon this framework, we further develop a dedicated two-stage training paradigm tailored for recommender agents. In the first stage, we introduce Recommendation-Oriented Trajectory Activation, optimize the agentic recommendation ability under implicit feedback. In the second stage, Progressive Preference Refinement further refines the agent through bidirectional preference reasoning over self-bootstrapped hard pairs, progressively sharpening preference boundaries. Theoretical analysis and extensive experiments demonstrate the effectiveness of AgenticRec. Our code is available at https://anonymous.4open.science/r/AgenticRec-FB16.
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