A four-agent LLM pipeline (user understanding, natural language inference, context summarization, and ranking) improves retrieval-augmented product recommendations on Amazon data by up to 42% in NDCG@5 over recency and vanilla-RAG baselines.
Seller-side Outcome Fairness in Online Marketplaces
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abstract
This paper aims to investigate and achieve seller-side fairness within online marketplaces, where many sellers and their items are not sufficiently exposed to customers in an e-commerce platform. This phenomenon raises concerns regarding the potential loss of revenue associated with less exposed items as well as less marketplace diversity. We introduce the notion of seller-side outcome fairness and build an optimization model to balance collected recommendation rewards and the fairness metric. We then propose a gradient-based data-driven algorithm based on the duality and bandit theory. Our numerical experiments on real e-commerce data sets show that our algorithm can lift seller fairness measures while not hurting metrics like collected Gross Merchandise Value (GMV) and total purchases.
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ARAG: Agentic Retrieval Augmented Generation for Personalized Recommendation
A four-agent LLM pipeline (user understanding, natural language inference, context summarization, and ranking) improves retrieval-augmented product recommendations on Amazon data by up to 42% in NDCG@5 over recency and vanilla-RAG baselines.