Agentic RAG for Personalized Recommendation
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Paper summary
Introduces a multi-agent framework that enhances traditional RAG systems with reasoning agents tailored to user modeling and contextual ranking. Developed at Walmart Global Tech, ARAG reframes recommendations as a structured coordination problem between LLM agents.
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01
User Understanding Agent synthesizes user preferences from long-term and session behavior.
02
NLI Agent evaluates semantic alignment between candidate items and user intent.
03
Context Summary Agent condenses relevant item metadata.
04
Item Ranker Agent ranks final recommendations using all prior reasoning.