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        <title>RecomMind: Movie Recommendation Dialogue with Seeker’s Internal State</title>
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    <abstract>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 and experiment, we constructed RecomMind, a movie recommendation dialogue dataset with annotations of the seeker’s internal state at the entity level. Each entity has a first-person label annotated by the seeker and a second-person label annotated by the recommender. Our analysis based on RecomMind reveals that the success of recommendations is enhanced when recommenders mention entities that seekers do not know but are interested in. 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.</abstract>
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%0 Conference Proceedings
%T RecomMind: Movie Recommendation Dialogue with Seeker’s Internal State
%A Kodama, Takashi
%A Kiyomaru, Hirokazu
%A Huang, Yin Jou
%A Kurohashi, Sadao
%Y Hale, James
%Y Chawla, Kushal
%Y Garg, Muskan
%S Proceedings of the Second Workshop on Social Influence in Conversations (SICon 2024)
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F kodama-etal-2024-recommind
%X 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 and experiment, we constructed RecomMind, a movie recommendation dialogue dataset with annotations of the seeker’s internal state at the entity level. Each entity has a first-person label annotated by the seeker and a second-person label annotated by the recommender. Our analysis based on RecomMind reveals that the success of recommendations is enhanced when recommenders mention entities that seekers do not know but are interested in. 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.
%U https://aclanthology.org/2024.sicon-1.4
%P 46-63

Markdown (Informal)

[RecomMind: Movie Recommendation Dialogue with Seeker’s Internal State](https://aclanthology.org/2024.sicon-1.4) (Kodama et al., SICon 2024)

• RecomMind: Movie Recommendation Dialogue with Seeker’s Internal State (Kodama et al., SICon 2024)

ACL

• Takashi Kodama, Hirokazu Kiyomaru, Yin Jou Huang, and Sadao Kurohashi. 2024. RecomMind: Movie Recommendation Dialogue with Seeker’s Internal State. In Proceedings of the Second Workshop on Social Influence in Conversations (SICon 2024), pages 46–63, Miami, Florida, USA. Association for Computational Linguistics.