TY - GEN
T1 - Critiquing for Music Exploration in Conversational Recommender Systems
AU - Cai, Wanling
AU - Jin, Yucheng
AU - Chen, Li
N1 - Funding Information:
This work was supported by Hong Kong Baptist University IR-CMS Project (IRCMS/19-20/D05). We also thank all participants for their time in taking part in our experiment, and reviewers for their valuable comments on our paper.
PY - 2021/4/14
Y1 - 2021/4/14
N2 - Dialogue-based conversational recommender systems allow users to give language-based feedback on the recommended item, which has great potential for supporting users to explore the space of recommendations through conversation. In this work, we consider incorporating critiquing techniques into conversational systems to facilitate users' exploration of music recommendations. Thus, we have developed a music chatbot with three system variants, which are respectively featured with three different critiquing techniques, i.e., user-initiated critiquing (UC), progressive system-suggested critiquing (Progressive SC), and cascading system-suggested critiquing (Cascading SC). We conducted a between-subject study (N=107) to compare these three types of systems with regards to music exploration in terms of user perception and user interaction. Results show that both UC and SC are useful for music exploration, while users perceive higher diversity of recommendations with the system that offers Cascading SC and perceive more serendipitous with the system that offers Progressive SC. In addition, we find that the critiquing techniques significantly moderate the relationships between some interaction metrics (e.g., number of listened songs, number of dialogue turns) and users' perceived helpfulness and serendipity during music exploration.
AB - Dialogue-based conversational recommender systems allow users to give language-based feedback on the recommended item, which has great potential for supporting users to explore the space of recommendations through conversation. In this work, we consider incorporating critiquing techniques into conversational systems to facilitate users' exploration of music recommendations. Thus, we have developed a music chatbot with three system variants, which are respectively featured with three different critiquing techniques, i.e., user-initiated critiquing (UC), progressive system-suggested critiquing (Progressive SC), and cascading system-suggested critiquing (Cascading SC). We conducted a between-subject study (N=107) to compare these three types of systems with regards to music exploration in terms of user perception and user interaction. Results show that both UC and SC are useful for music exploration, while users perceive higher diversity of recommendations with the system that offers Cascading SC and perceive more serendipitous with the system that offers Progressive SC. In addition, we find that the critiquing techniques significantly moderate the relationships between some interaction metrics (e.g., number of listened songs, number of dialogue turns) and users' perceived helpfulness and serendipity during music exploration.
KW - conversational interaction
KW - conversational recommender systems
KW - critiquing
KW - music exploration
UR - http://www.scopus.com/inward/record.url?scp=85104515004&partnerID=8YFLogxK
U2 - 10.1145/3397481.3450657
DO - 10.1145/3397481.3450657
M3 - Conference proceeding
AN - SCOPUS:85104515004
T3 - International Conference on Intelligent User Interfaces, Proceedings IUI
SP - 480
EP - 490
BT - 26th International Conference on Intelligent User Interfaces, IUI 2021
PB - Association for Computing Machinery (ACM)
T2 - 26th International Conference on Intelligent User Interfaces: Where HCI Meets AI, IUI 2021
Y2 - 14 April 2021 through 17 April 2021
ER -