ARAG: Agentic Retrieval Augmented Generation for Personalized Recommendation
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arXiv
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| Autori principali: | , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866918121950937088 |
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| author | Maragheh, Reza Yousefi Vadla, Pratheek Gupta, Priyank Zhao, Kai Inan, Aysenur Yao, Kehui Xu, Jianpeng Kanumala, Praveen Cho, Jason Kumar, Sushant |
| author_facet | Maragheh, Reza Yousefi Vadla, Pratheek Gupta, Priyank Zhao, Kai Inan, Aysenur Yao, Kehui Xu, Jianpeng Kanumala, Praveen Cho, Jason Kumar, Sushant |
| contents | Retrieval-Augmented Generation (RAG) has shown promise in enhancing recommendation systems by incorporating external context into large language model prompts. However, existing RAG-based approaches often rely on static retrieval heuristics and fail to capture nuanced user preferences in dynamic recommendation scenarios. In this work, we introduce ARAG, an Agentic Retrieval-Augmented Generation framework for Personalized Recommendation, which integrates a multi-agent collaboration mechanism into the RAG pipeline. To better understand the long-term and session behavior of the user, ARAG leverages four specialized LLM-based agents: a User Understanding Agent that summarizes user preferences from long-term and session contexts, a Natural Language Inference (NLI) Agent that evaluates semantic alignment between candidate items retrieved by RAG and inferred intent, a context summary agent that summarizes the findings of NLI agent, and an Item Ranker Agent that generates a ranked list of recommendations based on contextual fit. We evaluate ARAG accross three datasets. Experimental results demonstrate that ARAG significantly outperforms standard RAG and recency-based baselines, achieving up to 42.1% improvement in NDCG@5 and 35.5% in Hit@5. We also, conduct an ablation study to analyse the effect by different components of ARAG. Our findings highlight the effectiveness of integrating agentic reasoning into retrieval-augmented recommendation and provide new directions for LLM-based personalization. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_21931 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ARAG: Agentic Retrieval Augmented Generation for Personalized Recommendation Maragheh, Reza Yousefi Vadla, Pratheek Gupta, Priyank Zhao, Kai Inan, Aysenur Yao, Kehui Xu, Jianpeng Kanumala, Praveen Cho, Jason Kumar, Sushant Information Retrieval Artificial Intelligence Computation and Language Multiagent Systems I.2.11; I.2.7; H.3.3 Retrieval-Augmented Generation (RAG) has shown promise in enhancing recommendation systems by incorporating external context into large language model prompts. However, existing RAG-based approaches often rely on static retrieval heuristics and fail to capture nuanced user preferences in dynamic recommendation scenarios. In this work, we introduce ARAG, an Agentic Retrieval-Augmented Generation framework for Personalized Recommendation, which integrates a multi-agent collaboration mechanism into the RAG pipeline. To better understand the long-term and session behavior of the user, ARAG leverages four specialized LLM-based agents: a User Understanding Agent that summarizes user preferences from long-term and session contexts, a Natural Language Inference (NLI) Agent that evaluates semantic alignment between candidate items retrieved by RAG and inferred intent, a context summary agent that summarizes the findings of NLI agent, and an Item Ranker Agent that generates a ranked list of recommendations based on contextual fit. We evaluate ARAG accross three datasets. Experimental results demonstrate that ARAG significantly outperforms standard RAG and recency-based baselines, achieving up to 42.1% improvement in NDCG@5 and 35.5% in Hit@5. We also, conduct an ablation study to analyse the effect by different components of ARAG. Our findings highlight the effectiveness of integrating agentic reasoning into retrieval-augmented recommendation and provide new directions for LLM-based personalization. |
| title | ARAG: Agentic Retrieval Augmented Generation for Personalized Recommendation |
| topic | Information Retrieval Artificial Intelligence Computation and Language Multiagent Systems I.2.11; I.2.7; H.3.3 |
| url | https://arxiv.org/abs/2506.21931 |