research-article

Critiquing for Music Exploration in Conversational Recommender Systems

Authors:

Li ChenAuthors Info & Claims

IUI '21: Proceedings of the 26th International Conference on Intelligent User Interfaces

Pages 480 - 490

https://doi.org/10.1145/3397481.3450657

Published: 14 April 2021 Publication History

Abstract

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.

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Cited By

Jin YChen LCai WZhao X(2024)CRS-Que: A User-centric Evaluation Framework for Conversational Recommender SystemsACM Transactions on Recommender Systems10.1145/36315342:1(1-34)Online publication date: 7-Mar-2024
https://dl.acm.org/doi/10.1145/3631534
Jin YCai WChen LZhang YDoherty GJiang T(2024)Exploring the Design of Generative AI in Supporting Music-based Reminiscence for Older AdultsProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642800(1-17)Online publication date: 11-May-2024
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Ma YZiegler J(2024)Investigating meta-intents: user interaction preferences in conversational recommender systemsUser Modeling and User-Adapted Interaction10.1007/s11257-024-09411-3Online publication date: 24-Sep-2024
https://doi.org/10.1007/s11257-024-09411-3
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Critiquing for Music Exploration in Conversational Recommender Systems

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cover image ACM Conferences

IUI '21: Proceedings of the 26th International Conference on Intelligent User Interfaces

April 2021

618 pages

ISBN:9781450380171

DOI:10.1145/3397481

Copyright © 2021 ACM.

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Published: 14 April 2021

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April 14 - 17, 2021

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Overall Acceptance Rate 746 of 2,811 submissions, 27%

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Cited By

Jin YChen LCai WZhao X(2024)CRS-Que: A User-centric Evaluation Framework for Conversational Recommender SystemsACM Transactions on Recommender Systems10.1145/36315342:1(1-34)Online publication date: 7-Mar-2024
https://dl.acm.org/doi/10.1145/3631534
Jin YCai WChen LZhang YDoherty GJiang T(2024)Exploring the Design of Generative AI in Supporting Music-based Reminiscence for Older AdultsProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642800(1-17)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642800
Ma YZiegler J(2024)Investigating meta-intents: user interaction preferences in conversational recommender systemsUser Modeling and User-Adapted Interaction10.1007/s11257-024-09411-3Online publication date: 24-Sep-2024
https://doi.org/10.1007/s11257-024-09411-3
Cai WJin YZhao XChen L(2023)“Listen to Music, Listen to Yourself”: Design of a Conversational Agent to Support Self-Awareness While Listening to MusicProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3581427(1-19)Online publication date: 19-Apr-2023
https://dl.acm.org/doi/10.1145/3544548.3581427
Hegiansyah MBaizal Z(2023)Ontology-Based Conversational Recommender System for Smartphone Domain2023 11th International Conference on Information and Communication Technology (ICoICT)10.1109/ICoICT58202.2023.10262611(52-56)Online publication date: 23-Aug-2023
https://doi.org/10.1109/ICoICT58202.2023.10262611
Liang YWillemsen M(2022)Exploring the longitudinal effects of nudging on users’ music genre exploration behavior and listening preferencesProceedings of the 16th ACM Conference on Recommender Systems10.1145/3523227.3546772(3-13)Online publication date: 12-Sep-2022
https://dl.acm.org/doi/10.1145/3523227.3546772
Cai WJin YChen L(2022)Impacts of Personal Characteristics on User Trust in Conversational Recommender SystemsProceedings of the 2022 CHI Conference on Human Factors in Computing Systems10.1145/3491102.3517471(1-14)Online publication date: 29-Apr-2022
https://dl.acm.org/doi/10.1145/3491102.3517471
Cai WJin YChen L(2022)Task-Oriented User Evaluation on Critiquing-Based Recommendation ChatbotsIEEE Transactions on Human-Machine Systems10.1109/THMS.2021.313167452:3(354-366)Online publication date: Jun-2022
https://doi.org/10.1109/THMS.2021.3131674
Liang YWillemsen M(2022)Promoting Music Exploration through Personalized Nudging in a Genre Exploration RecommenderInternational Journal of Human–Computer Interaction10.1080/10447318.2022.210806039:7(1495-1518)Online publication date: 21-Aug-2022
https://doi.org/10.1080/10447318.2022.2108060
Jannach DChen L(2022)Conversational recommendationAI Magazine10.1002/aaai.1205943:2(151-163)Online publication date: 23-Jun-2022
https://dl.acm.org/doi/10.1002/aaai.12059

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