让大模型自动推荐并解释理由，无需专门训练。
AgenticRAG: Tool-Augmented Foundation Models for Zero-Shot Explainable Recommender Systems
- 用工具调用+检索生成+思维链，让模型自主推理推荐。
- 在三个数据集上推荐效果提升0.4%到1.6%，解释性更强。
- 适合需要透明推荐的场景，如电商、内容平台。
基础模型虽已革新人工智能，但在推荐系统中仍受限于推理不透明和知识不足。本文提出AgenticRAG框架，结合工具增强型基础模型与检索增强生成，实现零样本可解释推荐。该方法融合外部工具调用、知识检索和思维链推理，构建无需任务特定训练的自主推荐代理，实现透明决策。在三个真实数据集上的实验表明，AgenticRAG在推荐性能上持续优于现有基线：Amazon Electronics上NDCG@10提升0.4%，MovieLens-1M上提升0.8%，Yelp上提升1.6%。该框架在保持传统方法相当计算效率的同时，展现出更优的可解释性。
原文摘要 · Abstract (English)
Foundation models have revolutionized artificial intelligence, yet their application in recommender systems remains limited by reasoning opacity and knowledge constraints. This paper introduces AgenticRAG, a novel framework that combines tool-augmented foundation models with retrieval-augmented generation for zero-shot explainable recommendations. Our approach integrates external tool invocation, knowledge retrieval, and chain-of-thought reasoning to create autonomous recommendation agents capable of transparent decision-making without task-specific training. Experimental results on three real-world datasets demonstrate that AgenticRAG achieves consistent improvements over state-of-the-art baselines, with NDCG@10 improvements of 0.4\% on Amazon Electronics, 0.8\% on MovieLens-1M, and 1.6\% on Yelp datasets. The framework exhibits superior explainability while maintaining computational efficiency comparable to traditional methods.
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