AgenticRAG: Tool-Augmented Foundation Models for Zero-Shot Explainable Recommender Systems
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866915531765841920 |
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| author | Ma, Bo Li, Hang Hu, ZeHua Gui, XiaoFan Liu, LuYao Liu, Simon |
| author_facet | Ma, Bo Li, Hang Hu, ZeHua Gui, XiaoFan Liu, LuYao Liu, Simon |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_02668 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AgenticRAG: Tool-Augmented Foundation Models for Zero-Shot Explainable Recommender Systems Ma, Bo Li, Hang Hu, ZeHua Gui, XiaoFan Liu, LuYao Liu, Simon Information Retrieval Artificial Intelligence 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. |
| title | AgenticRAG: Tool-Augmented Foundation Models for Zero-Shot Explainable Recommender Systems |
| topic | Information Retrieval Artificial Intelligence |
| url | https://arxiv.org/abs/2510.02668 |