TY - GEN
T1 - Key Qualities of Conversational Recommender Systems
T2 - 9th International User Modeling, Adaptation and Personalization Human-Agent Interaction, HAI 2021
AU - Jin, Yucheng
AU - Chen, Li
AU - Cai, Wanling
AU - Pu, Pearl
N1 - Funding Information:
The work was supported by two research grants: HKBU IRCMS/19-20/D05 and RGC/HKBU12201620.
Publisher Copyright:
© 2021 ACM.
PY - 2021/11/9
Y1 - 2021/11/9
N2 - An increasing number of recommender systems enable conversational interaction to enhance the system's overall user experience (UX). However, it is unclear what qualities of a conversational recommender system (CRS) are essential to determine the success of a CRS. This paper presents a model to capture the key qualities of conversational recommender systems and their related user experience aspects. Our model incorporates the characteristics of conversations (such as adaptability, understanding, response quality, rapport, humanness, etc.) in four major user experience dimensions of the recommender system: User Perceived Qualities, User Belief, User Attitudes, and Behavioral Intentions. Following the psychometric modeling method, we validate the combined metrics using the data collected from an online user study of a conversational music recommender system. The user study results 1) support the consistency, validity, and reliability of the model that identifies seven key qualities of a CRS; and 2) reveal how conversation constructs interact with recommendation constructs to influence the overall user experience of a CRS. We believe that the key qualities identified in the model help practitioners design and evaluate conversational recommender systems.
AB - An increasing number of recommender systems enable conversational interaction to enhance the system's overall user experience (UX). However, it is unclear what qualities of a conversational recommender system (CRS) are essential to determine the success of a CRS. This paper presents a model to capture the key qualities of conversational recommender systems and their related user experience aspects. Our model incorporates the characteristics of conversations (such as adaptability, understanding, response quality, rapport, humanness, etc.) in four major user experience dimensions of the recommender system: User Perceived Qualities, User Belief, User Attitudes, and Behavioral Intentions. Following the psychometric modeling method, we validate the combined metrics using the data collected from an online user study of a conversational music recommender system. The user study results 1) support the consistency, validity, and reliability of the model that identifies seven key qualities of a CRS; and 2) reveal how conversation constructs interact with recommendation constructs to influence the overall user experience of a CRS. We believe that the key qualities identified in the model help practitioners design and evaluate conversational recommender systems.
KW - conversational recommender systems
KW - questionnaire
KW - Recommender systems
KW - user experience
KW - user-centric evaluation.
UR - http://www.scopus.com/inward/record.url?scp=85119339742&partnerID=8YFLogxK
U2 - 10.1145/3472307.3484164
DO - 10.1145/3472307.3484164
M3 - Conference proceeding
AN - SCOPUS:85119339742
T3 - Proceedings of International Conference on Human-Agent Interaction
SP - 93
EP - 102
BT - HAI 2021 - Proceedings of the 9th International Conference on Human-Agent Interaction
A2 - Ogawa, Kohei
A2 - Yonezawa, Tomoko
A2 - Lucas, Gale M.
A2 - Osawa, Hirotaka
A2 - Johal, Wafa
A2 - Shiomi, Masahiro
PB - Association for Computing Machinery (ACM)
Y2 - 9 November 2021 through 11 November 2021
ER -