New Framework Optimizes Prompts for Conversational Recommender User Simulators

Conversational recommender systems (CRSs) are crucial for future intelligent recommendation, allowing users to dynamically refine preferences. However, evaluating these systems and acquiring sufficient training data are significant hurdles. Traditional human studies are expensive and time-consuming, while interaction data often faces privacy restrictions. Large language model (LLM) based user simulators offer a potential solution by generating synthetic interactions for both evaluation and training. Existing LLM-based simulators, however, suffer from systematic positive bias, data leakage, and a lack of diverse user behaviors. They also rely heavily on manual prompt engineering, which demands extensive domain expertise and is prone to fragility. This new research introduces a framework designed to automatically optimize prompts for these LLM-based user simulators. The proposed multi-objective framework aims to mitigate the aforementioned issues by generating more realistic and diverse synthetic user interactions. Experimental results demonstrate that this automated prompt optimization significantly improves the behavioral alignment of simulated users with actual human interaction patterns, outperforming baseline methods across various prompt settings.