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E-commerce pre-sales dialogue aims to understand and elicit user needs andpreferences for the items they are seeking so as to provide appropriaterecommendations. Conversational recommender systems (CRSs) learn userrepresentation and provide accurate recommendations based on dialogue context,but rely on external knowledge. Large language models (LLMs) generate responsesthat mimic pre-sales dialogues after fine-tuning, but lack domain-specificknowledge for accurate recommendations. Intuitively, the strengths of LLM andCRS in E-commerce pre-sales dialogues are complementary, yet no previous workhas explored this. This paper investigates the effectiveness of combining LLMand CRS in E-commerce pre-sales dialogues, proposing two collaboration methods:CRS assisting LLM and LLM assisting CRS. We conduct extensive experiments on areal-world dataset of Ecommerce pre-sales dialogues. We analyze the impact oftwo collaborative approaches with two CRSs and two LLMs on four tasks ofEcommerce pre-sales dialogue. We find that collaborations between CRS and LLMcan be very effective in some cases.

Conversational Recommender System and Large Language Model Are Made for Each Other in E-commerce Pre-sales Dialogue