Retail-GPT: leveraging Retrieval Augmented Generation (RAG) for building E-commerce Chat Assistants
This work addresses the need for virtual sales agents in retail e-commerce, but it appears incremental as it applies existing RAG methods to a new domain without introducing major innovations.
The authors tackled the problem of enhancing user engagement in retail e-commerce by developing Retail-GPT, an open-source RAG-based chatbot that guides product recommendations and assists with cart operations, resulting in a system adaptable to various e-commerce domains without reliance on specific platforms.
This work presents Retail-GPT, an open-source RAG-based chatbot designed to enhance user engagement in retail e-commerce by guiding users through product recommendations and assisting with cart operations. The system is cross-platform and adaptable to various e-commerce domains, avoiding reliance on specific chat applications or commercial activities. Retail-GPT engages in human-like conversations, interprets user demands, checks product availability, and manages cart operations, aiming to serve as a virtual sales agent and test the viability of such assistants across different retail businesses.