A model of social explanations for a conversational movie recommendation system

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationHAI 2019 - Proceedings of the 7th International Conference on Human-Agent Interaction
PublisherAssociation for Computing Machinery, Inc
Pages135-143
Number of pages9
ISBN (Electronic)9781450369220
DOIs
Publication statusPublished - 2019 Sep 25
Event7th International Conference on Human-Agent Interaction, HAI 2019 - Kyoto, Japan
Duration: 2019 Oct 62019 Oct 10

Publication series

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

Conference

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

Keywords

  • Conversational recommendation system
  • Explanation
  • Socially-aware

ASJC Scopus subject areas

  • Artificial Intelligence
  • Human-Computer Interaction
  • Software

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