  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.
