Large language models (LLMs) have recently been adopted for recommendations due to their ability to understand user intent and item semantics. However, LLM-based recommender systems often rely on parametric knowledge and suffer from outdated knowledge, motivating knowledge graph retrieval-augmented generation (KG-RAG) to ground recommendations on structured, up-to-date KGs. Despite this promise, effective KG-RAG in recommendations faces great challenges. First, users' queries vary in complexity and require KG knowledge at different granularities, whereas existing methods adopt a one-size-fits-all retrieval strategy, leading to over-retrieval for simple queries and under-retrieval for complex ones. In addition, augmenting LLMs with KG knowledge requires translating graph-structured data into linear text, which may introduce noise and cause structural information loss. Moreover, the selection of retrieval granularity lacks direct supervision and must be inferred from the final recommendation after alignment and downstream utilization, making query-aware retrieval hard to learn end-to-end. To address these issues, we propose MixRAGRec, a cooperative multi-agent framework for KG-RAG recommendations. MixRAGRec integrates a Mixture-of-Experts Retrieval Agent that routes each query to a KG retrieval expert with different granularities, a Knowledge Preference Alignment Agent that converts structured knowledge into LLM-friendly natural language, and a Contrastive Learning-reinforced Recommendation Agent trained with contrastive preference feedback. Notably, we introduce Mixture-of-Experts Multi-Agent Policy Optimization (MMAPO) to train three agents under a unified objective. Extensive experiments on real-world datasets demonstrate the effectiveness of our framework.

翻译：大语言模型（LLM）近期因具备理解用户意图与项目语义的能力而被应用于推荐系统。然而，基于LLM的推荐系统常依赖参数化知识，存在知识过时问题，因此需要知识图谱检索增强生成（KG-RAG）将推荐任务锚定于结构化的最新知识图谱。尽管前景广阔，但在推荐中实现高效KG-RAG仍面临重大挑战。首先，用户查询复杂度各异，对知识图谱知识的需求粒度不同，而现有方法采用"一刀切"的检索策略，导致简单查询过度检索、复杂查询欠检索。其次，以知识图谱增强LLM需将图结构数据转化为线性文本，可能引入噪声并造成结构信息损失。此外，检索粒度的选择缺乏直接监督，必须通过对齐与下游利用后的最终推荐结果间接推断，使得查询感知型检索难以端到端学习。为解决上述问题，我们提出MixRAGRec——一种面向KG-RAG推荐的多智能体协作框架。MixRAGRec集成了混合专家检索智能体（针对不同查询粒度路由至相应检索专家）、知识偏好对齐智能体（将结构化知识转化为LLM友好的自然语言）以及对比学习增强推荐智能体（通过对比偏好反馈训练）。值得注意的是，我们引入混合专家多智能体策略优化（MMAPO）方法，在统一优化目标下训练三个智能体。在真实数据集上的大量实验证实了我们框架的有效性。