research-article

Context-aware mobile music recommendation for daily activities

Authors:

David Rosenblum,

Ye WangAuthors Info & Claims

MM '12: Proceedings of the 20th ACM international conference on Multimedia

Pages 99 - 108

https://doi.org/10.1145/2393347.2393368

Published: 29 October 2012 Publication History

Abstract

Existing music recommendation systems rely on collaborative filtering or content-based technologies to satisfy users' long-term music playing needs. Given the popularity of mobile music devices with rich sensing and wireless communication capabilities, we present in this paper a novel approach to employ contextual information collected with mobile devices for satisfying users' short-term music playing needs. We present a probabilistic model to integrate contextual information with music content analysis to offer music recommendation for daily activities, and we present a prototype implementation of the model. Finally, we present evaluation results demonstrating good accuracy and usability of the model and prototype.

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Cited By

Popereshnyak SFuks VTsurkan AZhebka V(2024)Use of artificial intelligence sn the application for working with musical notesPROBLEMS IN PROGRAMMING10.15407/pp2024.02-03.384(384-391)Online publication date: Sep-2024
https://doi.org/10.15407/pp2024.02-03.384
Chen YYuan HMa BWang LQian Y(2024)Beyond Songs: Analyzing User Sentiment through Music Playlists and Multimodal DataACM Transactions on Multimedia Computing, Communications, and Applications10.1145/370834621:3(1-24)Online publication date: 12-Dec-2024
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Index Terms

Context-aware mobile music recommendation for daily activities

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Published In

cover image ACM Conferences

MM '12: Proceedings of the 20th ACM international conference on Multimedia

October 2012

1584 pages

ISBN:9781450310895

DOI:10.1145/2393347

General Chairs:
Noboru Babaguchi
Osaka University, Japan
,
Kiyoharu Aizawa
The University of Tokyo, Japan
,
John Smith
IBM, USA
,
Program Chairs:
Shin'ichi Satoh
National Institute of Informatics, Japan
,
Thomas Plagemann
University of Oslo, Norway
,
Xian-Sheng Hua
Microsoft, USA
,
Rong Yan
Facebook, USA

Copyright © 2012 ACM.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 29 October 2012

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MM '12: ACM Multimedia Conference

October 29 - November 2, 2012

Nara, Japan

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Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

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Cited By

Popereshnyak SFuks VTsurkan AZhebka V(2024)Use of artificial intelligence sn the application for working with musical notesPROBLEMS IN PROGRAMMING10.15407/pp2024.02-03.384(384-391)Online publication date: Sep-2024
https://doi.org/10.15407/pp2024.02-03.384
Chen YYuan HMa BWang LQian Y(2024)Beyond Songs: Analyzing User Sentiment through Music Playlists and Multimodal DataACM Transactions on Multimedia Computing, Communications, and Applications10.1145/370834621:3(1-24)Online publication date: 12-Dec-2024
https://dl.acm.org/doi/10.1145/3708346
Xu JWu TLi J(2024)An R&D Partner Recommendation Framework Based on a Knowledge Context Hypernetwork for Engineering Technological InnovationIEEE Transactions on Engineering Management10.1109/TEM.2023.329595171(9938-9952)Online publication date: 2024
https://doi.org/10.1109/TEM.2023.3295951
Dai SShao NZhu JZhang XDong ZXu JDai QWen J(2024)Modeling User Attention in Music Recommendation2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00064(761-774)Online publication date: 13-May-2024
https://doi.org/10.1109/ICDE60146.2024.00064
Goyal PSeale ACharubin KBennett QBryce R(2024)AI-Powered Smartphone Context and High-Utility Itemset Mining for Enhanced App Testing and PersonalizationITNG 2024: 21st International Conference on Information Technology-New Generations10.1007/978-3-031-56599-1_54(427-435)Online publication date: 11-Mar-2024
https://doi.org/10.1007/978-3-031-56599-1_54
Assuncao WPrates RZaina L(2023)From User Context to Tailored Playlists: A User Centered Approach to Improve Music Recommendation SystemProceedings of the XXII Brazilian Symposium on Human Factors in Computing Systems10.1145/3638067.3638084(1-11)Online publication date: 16-Oct-2023
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Assunção WPrates RZaina L(2023)Managing Cold-Start Issues in Music Recommendation Systems: An Approach Based on User ExperienceCompanion Proceedings of the 2023 ACM SIGCHI Symposium on Engineering Interactive Computing Systems10.1145/3596454.3597180(31-37)Online publication date: 27-Jun-2023
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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
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Assi KMeegahapola LDroz WKun PDe Götzen ABidoglia MStares SGaskell GChagnaa AGanbold AZundui TCaprini CMiorandi DZarza JHume ACernuzzi LBison IRodas Britez MBusso MChenu-Abente RGiunchiglia FGatica-Perez D(2023)Complex Daily Activities, Country-Level Diversity, and Smartphone Sensing: A Study in Denmark, Italy, Mongolia, Paraguay, and UKProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3581190(1-23)Online publication date: 19-Apr-2023
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Li FHu X(2023)Background Music for Studying: A Naturalistic Experiment on Music Characteristics and User PerceptionIEEE MultiMedia10.1109/MMUL.2023.324320930:1(62-72)Online publication date: 1-Jan-2023
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