ARAG: Agentic Retrieval Augmented Generation for Personalized Recommendation
- Published
- Source
- arXiv
- Paper number
- 068
- Field
- RAG / Agents
- arXiv ID
- 2506.21931
Key points
- Existing RAG systems for personalized recommendation rely on simple retrieval mechanisms, such as cosine similarity, that fail to capture nuanced user preferences.
- Existing RAG methods struggle to understand implicit preferences, process long-form user documents, and dynamically rank items based on multifaceted contextual factors.
- Using LLMs directly for recommendation is computationally expensive, prone to hallucination, and constrained by fixed knowledge cutoffs, so robust grounding is needed.
- ARAG proposes a multi-agent framework in which specialized LLM agents collaborate to refine context retrieval and item ranking for personalized recommendation.
- It uses a User Understanding Agent (UUA), a Natural Language Inference (NLI) agent, a Context Summarization Agent (CSA), and an Item Ranking Agent (IRA) to decompose the complex recommendation task.
- The agents operate in a blackboard-style collaboration, with UUA and NLI running in parallel, followed by CSA and then IRA, to synthesize user preferences and align them with candidate items.
Paper links
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