标题： AgentDR 基于大语言模型代理的隐式物品间关系动态推荐 显示英文标题

标题： AgentDR Dynamic Recommendation with Implicit Item-Item Relations via LLM-based Agents

摘要： 最近的基于代理的推荐框架旨在通过结合记忆机制和提示策略来模拟用户行为，但它们在幻觉不存在的商品和全目录排序方面存在困难。 此外，利用大语言模型（LLM）的常识推理来通过商品之间的替代和补充关系捕捉用户意图，这是一个很大程度上未被探索的机会，这些关系通常在数据集中是隐式的，传统的基于ID的推荐器难以捕捉。 在本工作中，我们提出了一种新颖的LLM代理框架AgenDR，该框架将LLM推理与可扩展的推荐工具相结合。 我们的方法将全排序任务委托给传统模型，同时利用LLM来（i）根据个性化的工具适用性整合多个推荐输出，并（ii）基于用户历史进行替代和补充关系的推理。 这种设计减轻了幻觉问题，能够扩展到大规模目录，并通过关系推理提高推荐的相关性。 通过在三个公开的杂货数据集上的广泛实验，我们展示了我们的框架在全排序性能方面表现优越，平均比其底层工具提高了两倍。 我们还引入了一种新的基于LLM的评估指标，该指标联合衡量语义对齐度和排序正确性。 显示英文摘要

摘要： Recent agent-based recommendation frameworks aim to simulate user behaviors by incorporating memory mechanisms and prompting strategies, but they struggle with hallucinating non-existent items and full-catalog ranking. Besides, a largely underexplored opportunity lies in leveraging LLMs'commonsense reasoning to capture user intent through substitute and complement relationships between items, which are usually implicit in datasets and difficult for traditional ID-based recommenders to capture. In this work, we propose a novel LLM-agent framework, AgenDR, which bridges LLM reasoning with scalable recommendation tools. Our approach delegates full-ranking tasks to traditional models while utilizing LLMs to (i) integrate multiple recommendation outputs based on personalized tool suitability and (ii) reason over substitute and complement relationships grounded in user history. This design mitigates hallucination, scales to large catalogs, and enhances recommendation relevance through relational reasoning. Through extensive experiments on three public grocery datasets, we show that our framework achieves superior full-ranking performance, yielding on average a twofold improvement over its underlying tools. We also introduce a new LLM-based evaluation metric that jointly measures semantic alignment and ranking correctness.