Enhancing E-Commerce with a RAG-Powered Conversational Recommender System

• Danilo Xavier Valle IFES
• Hilário Tomaz Alves de Oliveira IFES

DOI: https://doi.org/10.5753/eniac.2025.11835

Resumo

Conversational recommender systems have emerged as a promising approach to enhancing user experience in e-commerce by enabling interactive and personalized product discovery. This paper proposes a conversational recommender system for e-commerce that employs retrieval-augmented generation (RAG) to improve product recommendations based on natural language queries. Experiments were conducted using the ESCI-S dataset, an enriched version of the Amazon ESCI shopping queries dataset, to evaluate embedding models and large language models. The goal of this study is to assess the effectiveness of an RAG-based conversational recommender system and to identify optimal configurations for enhanced performance in e-commerce applications.

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