The promise of *LLM-based user simulators* to improve conversational AI is hindered by a critical "realism gap," leading to systems that are optimized for simulated interactions, but may fail to perform well in the real world. We introduce *ConvApparel*, a new dataset of human-AI conversations designed to address this gap. Its unique dual-agent data collection protocol, using both "good" and "bad" recommenders, enables counterfactual validation by capturing a wide spectrum of user experiences, enriched with first-person annotations of user satisfaction.We propose a comprehensive validation framework that combines *statistical alignment*, a *human-likeness score*, and *counterfactual validation* to test for generalization.Our experiments reveal a significant realism gap across all simulators. However, the framework also shows that data-driven simulators outperform a prompted baseline, particularly in counterfactual validation where they adapt more realistically to unseen behaviors, suggesting they embody more robust, if imperfect, user models.

Conversational recommendation has recently attracted significant attention. As systems must understand users’ preferences, training them has called for conversational corpora, typically derived from task-oriented conversations. We observe that such corpora often do not reflect how people naturally describe preferences. We present a new approach to obtaining user preferences in dialogue: Coached Conversational Preference Elicitation. It allows collection of natural yet structured conversational preferences. Studying the dialogues in one domain, we present a brief quantitative analysis of how people describe movie preferences at scale. Demonstrating the methodology, we release the CCPE-M dataset to the community with over 500 movie preference dialogues expressing over 10,000 preferences.