Prompt Optimization for User Simulation in Conversational Recommender Systems: A Multi-Objective Framework
Conversational recommender systems (CRSs) are a core component of next-generation intelligent recommender systems because they enable users to actively elicit preferences, clarify intentions, and adapt recommendations in real time. However, there are two key obstacles in the CRS domain: evaluation and access to training data. Evaluating CRSs through real human studies is more critical than for traditional recommender systems, yet such studies are both costly and time-consuming. Moreover, CRS interaction data are often difficult to obtain for model training due to privacy concerns. Large language model (LLM)-based user simulators have shown promise in addressing both challenges by generati...
| Authors | Nipun B Nair, Tongtong Wu, Weiqing Wang |
|---|---|
| Primary category | cs.IR |
| Published | 8 May 2026 |
| Last updated at source | 8 May 2026 |
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