We present an extensible user simulation toolkit to facilitate automatic evaluation of conversational recommender systems. It builds on an established agenda-based approach and extends it with several novel elements, including user satisfaction prediction, persona and context modeling, and conditional natural language generation. We showcase the toolkit with a pre-existing movie recommender system and demonstrate its ability to simulate dialogues that mimic real conversations, while requiring only a handful of manually annotated dialogues as training data.

翻译：我们提出了一个可扩展的用户模拟工具包,以便利对对话建议系统进行自动评价,该工具包以既定的议程为基础的方法为基础,并包含若干新内容,包括用户满意度预测、个人和背景建模以及有条件的自然语言生成。 我们用一个先前存在的电影建议系统展示该工具包,并展示其模拟模拟模拟模拟真实对话的能力,同时只需要少量手动附加说明的对话作为培训数据。