A Socially-Aware Conversational Recommender System for Personalized Recipe Recommendations

Florian Pecune, Lucile Callebert, Stacy Marsella

研究成果: Conference contribution

抄録

One potential solution to help people change their eating behavior is to develop conversational systems able to recommend healthy recipes. Beyond the intrinsic quality of the recommendations themselves, various factors might also influence users? perception of a recommendation. Two of these factors are the conversational skills of the system and users' interaction modality. In this paper, we present Cora, a conversational system that recommends recipes aligned with its users? eating habits and current preferences. Users can interact with Cora in two different ways. They can select predefined answers by clicking on buttons to talk to Cora or write text in natural language. On the other hand, Cora can engage users through a social dialogue, or go straight to the point. We conduct an experiment to evaluate the impact of Cora's conversational skills and users' interaction mode on users' perception and intention to cook the recommended recipes. Our results show that a conversational recommendation system that engages its users through a rapport-building dialogue improves users' perception of the interaction as well as their perception of the system.

本文言語English
ホスト出版物のタイトルHAI 2020 - Proceedings of the 8th International Conference on Human-Agent Interaction
出版社Association for Computing Machinery, Inc
ページ78-86
ページ数9
ISBN(電子版)9781450380546
DOI
出版ステータスPublished - 2020 11 10
外部発表はい
イベント8th International Conference on Human-Agent Interaction, HAI 2020 - Virtual, Online, Australia
継続期間: 2020 11 102020 11 13

出版物シリーズ

名前HAI 2020 - Proceedings of the 8th International Conference on Human-Agent Interaction

Conference

Conference8th International Conference on Human-Agent Interaction, HAI 2020
国/地域Australia
CityVirtual, Online
Period20/11/1020/11/13

ASJC Scopus subject areas

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

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