让推荐系统像人一样动态选工具，适应用户兴趣变化。
ChainRec: An Agentic Recommender Learning to Route Tool Chains for Diverse and Evolving Interests
- 用规划器动态选工具顺序，不再固定流程。
- 冷启动和兴趣变化场景下，点击率提升显著。
- 适合需要自适应推荐的电商、社交平台使用。
大型语言模型（LLMs）正被越来越多地集成到推荐系统中，推动了基于代理和推理的推荐研究。然而，现有方法大多依赖固定工作流，在不同推荐场景中采用相同的推理过程。实际上，用户上下文差异很大——例如在冷启动或兴趣转变时，代理应能自适应决定下一步收集什么证据，而非遵循预设流程。为此，我们提出 ChainRec，一种利用规划器动态选择推理工具的代理推荐系统。ChainRec 从专家轨迹中构建标准化工具代理库，并通过监督微调与偏好优化训练规划器，以动态选择工具、确定顺序并判断停止时机。在 AgentRecBench 上对 Amazon、Yelp 和 Goodreads 的实验表明，ChainRec 在多个基线之上持续提升 Avg HR@{1,3,5}，尤其在冷启动和兴趣演变场景中表现突出。消融实验进一步验证了工具标准化与偏好优化规划的重要性。
原文摘要 · Abstract (English)
Large language models (LLMs) are increasingly integrated into recommender systems, motivating recent interest in agentic and reasoning-based recommendation. However, most existing approaches still rely on fixed workflows, applying the same reasoning procedure across diverse recommendation scenarios. In practice, user contexts vary substantially-for example, in cold-start settings or during interest shifts, so an agent should adaptively decide what evidence to gather next rather than following a scripted process. To address this, we propose ChainRec, an agentic recommender that uses a planner to dynamically select reasoning tools. ChainRec builds a standardized Tool Agent Library from expert trajectories. It then trains a planner using supervised fine-tuning and preference optimization to dynamically select tools, decide their order, and determine when to stop. Experiments on AgentRecBench across Amazon, Yelp, and Goodreads show that ChainRec consistently improves Avg HR@{1,3,5} over strong baselines, with especially notable gains in cold-start and evolving-interest scenarios. Ablation studies further validate the importance of tool standardization and preference-optimized planning.
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