A model of social explanations for a conversational movie recommendation system

Florian Pecune, Shruti Murali, Vivian Tsai, Yoichi Matsuyama, Justine Cassell

研究成果: Conference contribution

16 被引用数 (Scopus)

抄録

A critical aspect of any recommendation process is explaining the reasoning behind each recommendation. These explanations can not only improve users' experiences, but also change their perception of the recommendation quality. This work describes our human-centered design for our conversational movie recommendation agent, which explains its decisions as humans would. After exploring and analyzing a corpus of dyadic interactions, we developed a computational model of explanations. We then incorporated this model in the architecture of a conversational agent and evaluated the resulting system via a user experiment. Our results show that social explanations can improve the perceived quality of both the system and the interaction, regardless of the intrinsic quality of the recommendations.

本文言語English
ホスト出版物のタイトルHAI 2019 - Proceedings of the 7th International Conference on Human-Agent Interaction
出版社Association for Computing Machinery, Inc
ページ135-143
ページ数9
ISBN(電子版)9781450369220
DOI
出版ステータスPublished - 2019 9 25
イベント7th International Conference on Human-Agent Interaction, HAI 2019 - Kyoto, Japan
継続期間: 2019 10 62019 10 10

出版物シリーズ

名前HAI 2019 - Proceedings of the 7th International Conference on Human-Agent Interaction

Conference

Conference7th International Conference on Human-Agent Interaction, HAI 2019
国/地域Japan
CityKyoto
Period19/10/619/10/10

ASJC Scopus subject areas

  • 人工知能
  • 人間とコンピュータの相互作用
  • ソフトウェア

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