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Understanding Music Listening Intents During Daily Activities with Implications for Contextual Music Recommendation

Authors:

Sergey Volokhin,

Eugene AgichteinAuthors Info & Claims

CHIIR '18: Proceedings of the 2018 Conference on Human Information Interaction & Retrieval

Pages 313 - 316

https://doi.org/10.1145/3176349.3176885

Published: 01 March 2018 Publication History

Abstract

Why do we listen to music? This question has as many answers as there are people, which may vary by time of day, and the activity of the listener. We envision a contextual music search and recommendation system, which could suggest appropriate music to the user in the current context. As an important step in this direction, we set out to understand what are the users» intents for listening to music, and how they relate to common daily activities. To accomplish this, we conduct and analyze a survey of why and when people of different ages and in different countries listen to music. The resulting categories of common musical intents, and the associations of intents and activities, could be helpful for guiding the development and evaluation of contextual music recommendation systems.

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Cited By

Popescu AHolman A(2024)Loop and Enjoy: A Scoping Review of the Research on the Effects of Processing Fluency on Aesthetic Reactions to Auditory StimuliPsychological Reports10.1177/00332941241277474Online publication date: 29-Aug-2024
https://doi.org/10.1177/00332941241277474
Melder TSavery R(2024)Artificial Intelligence and Home Music Listening_An Overview And Future DirectionsProceedings of the 19th International Audio Mostly Conference: Explorations in Sonic Cultures10.1145/3678299.3678330(318-324)Online publication date: 18-Sep-2024
https://dl.acm.org/doi/10.1145/3678299.3678330
Grigorev VLi JMa WHe ZZhang MLiu YYan MZhang J(2024)SiTunes: A Situational Music Recommendation Dataset with Physiological and Psychological SignalsProceedings of the 2024 Conference on Human Information Interaction and Retrieval10.1145/3627508.3638343(417-421)Online publication date: 10-Mar-2024
https://dl.acm.org/doi/10.1145/3627508.3638343
Show More Cited By

Index Terms

Understanding Music Listening Intents During Daily Activities with Implications for Contextual Music Recommendation
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. HCI design and evaluation methods
      1. User studies
    2. HCI theory, concepts and models

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Published In

cover image ACM Conferences

CHIIR '18: Proceedings of the 2018 Conference on Human Information Interaction & Retrieval

March 2018

402 pages

ISBN:9781450349253

DOI:10.1145/3176349

General Chairs:
Chirag Shah
Rutgers University, USA
,
Nicholas J. Belkin
Rutgers University, USA
,
Program Chairs:
Katriina Byström
Oslo Metropolitan University, Norway
,
Jeff Huang
Brown University, USA
,
Falk Scholer
RMIT University, Australia

Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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SIGIR: ACM Special Interest Group on Information Retrieval

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SIGCHI: ACM Special Interest Group on Computer-Human Interaction

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 March 2018

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Short-paper

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CHIIR '18

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SIGIR

CHIIR '18: Conference on Human Information Interaction and Retrieval

March 11 - 15, 2018

NJ, New Brunswick, USA

Acceptance Rates

CHIIR '18 Paper Acceptance Rate 22 of 57 submissions, 39%;

Overall Acceptance Rate 55 of 163 submissions, 34%

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Citations

Cited By

Popescu AHolman A(2024)Loop and Enjoy: A Scoping Review of the Research on the Effects of Processing Fluency on Aesthetic Reactions to Auditory StimuliPsychological Reports10.1177/00332941241277474Online publication date: 29-Aug-2024
https://doi.org/10.1177/00332941241277474
Melder TSavery R(2024)Artificial Intelligence and Home Music Listening_An Overview And Future DirectionsProceedings of the 19th International Audio Mostly Conference: Explorations in Sonic Cultures10.1145/3678299.3678330(318-324)Online publication date: 18-Sep-2024
https://dl.acm.org/doi/10.1145/3678299.3678330
Grigorev VLi JMa WHe ZZhang MLiu YYan MZhang J(2024)SiTunes: A Situational Music Recommendation Dataset with Physiological and Psychological SignalsProceedings of the 2024 Conference on Human Information Interaction and Retrieval10.1145/3627508.3638343(417-421)Online publication date: 10-Mar-2024
https://dl.acm.org/doi/10.1145/3627508.3638343
Marey LSguerra BMoussallam M(2024)Modeling Activity-Driven Music Listening with PACEProceedings of the 2024 Conference on Human Information Interaction and Retrieval10.1145/3627508.3638299(346-351)Online publication date: 10-Mar-2024
https://dl.acm.org/doi/10.1145/3627508.3638299
Jin SLee J(2024)Effective music skip prediction based on late fusion architecture for user-interaction noiseExpert Systems with Applications10.1016/j.eswa.2023.122098238(122098)Online publication date: Mar-2024
https://doi.org/10.1016/j.eswa.2023.122098
Ma YGan M(2024)Sequential-hierarchical attention network: Exploring the hierarchical intention feature in POI recommendationWorld Wide Web10.1007/s11280-024-01295-y27:6Online publication date: 24-Sep-2024
https://dl.acm.org/doi/10.1007/s11280-024-01295-y
Lee EMin CLee JYu JKang SEl Saddik AMei TCucchiara RBertini MTobon Vallejo DAtrey PHossain M(2023)GrooveMeter: Enabling Music Engagement-aware Apps by Detecting Reactions to Daily Music Listening via Earable SensingProceedings of the 31st ACM International Conference on Multimedia10.1145/3581783.3611968(7728-7736)Online publication date: 26-Oct-2023
https://dl.acm.org/doi/10.1145/3581783.3611968
Yang YCapra R(2023)Nested Contexts of Music Information Retrieval: A Framework of Contextual FactorsProceedings of the 2023 Conference on Human Information Interaction and Retrieval10.1145/3576840.3578322(368-372)Online publication date: 19-Mar-2023
https://dl.acm.org/doi/10.1145/3576840.3578322
Meggetto FRevie CLevine JMoshfeghi Y(2023)Why People Skip Music? On Predicting Music Skips using Deep Reinforcement LearningProceedings of the 2023 Conference on Human Information Interaction and Retrieval10.1145/3576840.3578312(95-106)Online publication date: 19-Mar-2023
https://dl.acm.org/doi/10.1145/3576840.3578312
Rafique WHafid AQadir J(2023)Developing smart city services using intent‐aware recommendation systems: A surveyTransactions on Emerging Telecommunications Technologies10.1002/ett.472834:4Online publication date: 12-Jan-2023
https://doi.org/10.1002/ett.4728
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