ARAG: Agentic Retrieval Augmented Generation for Personalized Recommendation

• URL: http://arxiv.org/abs/2506.21931v2
• Date: Mon, 11 Aug 2025 16:24:36 GMT
• Title: ARAG: Agentic Retrieval Augmented Generation for Personalized Recommendation
• Abstract summary: ARAG is an Agentic Retrieval-Augmented Generation framework for Personalized Recommendation.<n>ARAG integrates a multi-agent collaboration mechanism into the RAG pipeline.<n>ARAG significantly outperforms standard RAG and recency-based baselines.
• Score: 9.099080929623156
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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