Humans pay careful attention to the interlocutor's internal state in dialogues. For example, in recommendation dialogues, we make recommendations while estimating the seeker's internal state, such as his/her level of knowledge and interest. Since there are no existing annotated resources for the analysis, we constructed RecMind, a Japanese movie recommendation dialogue dataset with annotations of the seeker's internal state at the entity level. Each entity has a subjective label annotated by the seeker and an objective label annotated by the recommender. RecMind also features engaging dialogues with long seeker's utterances, enabling a detailed analysis of the seeker's internal state. Our analysis based on RecMind reveals that entities that the seeker has no knowledge about but has an interest in contribute to recommendation success. We also propose a response generation framework that explicitly considers the seeker's internal state, utilizing the chain-of-thought prompting. The human evaluation results show that our proposed method outperforms the baseline method in both consistency and the success of recommendations.

在推荐对话中，我们通过估计寻求者的内部状态，如其知识水平和兴趣等，进行推荐。本论文构建了带有内部状态注释的日本电影推荐对话数据集RecMind，该数据集具有长寻求者话语并能够详细分析其内部状态的特点。基于RecMind的分析表明，寻求者对于没有了解但感兴趣的实体有助于推荐成功。同时，我们提出了一个考虑寻求者内部状态的应答生成框架，利用思维链提示。人工评估结果表明我们的方法在一致性和推荐成功方面优于基准方法。

RecMind: 求助者内部状态下的日本电影推荐对话