TY - GEN
T1 - A model of social explanations for a conversational movie recommendation system
AU - Pecune, Florian
AU - Murali, Shruti
AU - Tsai, Vivian
AU - Matsuyama, Yoichi
AU - Cassell, Justine
N1 - Funding Information:
This work was supported in part by funding from Oath and the IT R&D program of MSIP/IITP [2017-0-00255, Autonomous digital companion development]. We would also like to thank John Choi for his generous help, Yoo Jin Shin for refining annotations, and the members of Carnegie Mellon University’s ArticuLab for their feedback and support.
Publisher Copyright:
© 2019 ACM.
PY - 2019/9/25
Y1 - 2019/9/25
N2 - 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.
AB - 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.
KW - Conversational recommendation system
KW - Explanation
KW - Socially-aware
UR - http://www.scopus.com/inward/record.url?scp=85077120890&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85077120890&partnerID=8YFLogxK
U2 - 10.1145/3349537.3351899
DO - 10.1145/3349537.3351899
M3 - Conference contribution
AN - SCOPUS:85077120890
T3 - HAI 2019 - Proceedings of the 7th International Conference on Human-Agent Interaction
SP - 135
EP - 143
BT - HAI 2019 - Proceedings of the 7th International Conference on Human-Agent Interaction
PB - Association for Computing Machinery, Inc
T2 - 7th International Conference on Human-Agent Interaction, HAI 2019
Y2 - 6 October 2019 through 10 October 2019
ER -