Conversational Recommender System and Large Language Model Are Made for Each Other in E -commerce Pre-sales Dialogue (2023)

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

## related work we review the related work along two lines: (i) conversational recommendation and (ii) large language models (llms) for recommendation. ## results and analysis we conduct extensive experiments to explore the performance of collaborations of llms and crss on four tasks. we analyze impacts of collaborations on each task in turn. ## conclusions and future work in this paper, we investigated the integration of conversational recommender systems (crs) and large language models (llm) in e-commerce pre-sales dialogues. specifically, we proposed two collaboration strategies: "crs assisting llm" and "llm assisting crs". we evaluate the effectiveness of these collaborations between two llms and two crss on four tasks related to e-commerce presales dialogues. through extensive experiments and careful analysis, we found that the collaboration between crs and llm can be highly effective in certain scenarios, providing valuable insights for both research and practical applications in ecommerce pre-sales dialogues. additionally, our findings can inspire exploration of collaborations between general large models and private small models in other domains and areas. for future work, we plan to examine the collaboration between llms and crss across various categories. for instance, a crs within the shoes category could provide information to a llm in the fashion category, resulting in a recommended combination such as a dress, pants, and shoes.