Exploring Multi-LLM Collaboration to Power Conversational Recommender System: A Case Study of Dietary Recommendation

Abstract: Conversational recommender systems (CRS) are promising in delivering personalized recommendations by engaging users to share rich information about themselves, particularly in dietary recommendation, where various factors (e.g., food preferences, eating habits) needs to be considered. However, maintaining a coherent conversation for information collection and recommending healthy dishes tailored to different users remains challenging, even with the emerging large language models (LLMs). In this study, we explore multi-LLM collaboration—where multiple LLMs specialize in subtasks of a complex problem—to enhance a dietary CRS. Through an online experiment (N = 161), we compared multi-LLM collaboration with its single-LLM counterpart during the conversation and recommendation phases, evaluating system performance and participants’ experiences. We found multi-LLM collaboration equipped the conversation manager with greater adaptability to the conversation contexts, while powering the recommendation engine to deliver more nutritionally balanced and wide-range recommendations. Our discussion then focuses on the implications for designing user-centered CRS with LLMs.