research-article

Inferring personal traits from music listening history

Authors:

Yi-Hsuan YangAuthors Info & Claims

MIRUM '12: Proceedings of the second international ACM workshop on Music information retrieval with user-centered and multimodal strategies

Pages 31 - 36

https://doi.org/10.1145/2390848.2390856

Published: 02 November 2012 Publication History

Abstract

Nowadays, we often leave our personal information on the Internet without noticing it. People could learn things about you from these information. It has been reported that it is possible to infer some personal information from the web browsing records or from blog articles. As the music streaming services become increasingly popular, the music listening history of one person could be acquired easily. This paper investigates the possibility for a computer to automatically infer personal traits such as gender and age from the music listening history. Specifically, we consider three types of features for building the machine learning models, including 1) statistics of the listening timestamps, 2) song/artist metadata, and 3) song signal features, and evaluate the accuracy of binary age classification and gender classification utilizing a 1K-user dataset obtained from the online music service Last.fm. Our study brings about new insights into the human behavior of music listening, but also raises concern over the privacy issues involved in music streaming services.

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Cited By

Tricomi PPajola LPasa LConti MChua TNgo CKumar RLauw HKa-Wei Lee R(2024)"All of Me": Mining Users' Attributes from their Public Spotify PlaylistsCompanion Proceedings of the ACM Web Conference 202410.1145/3589335.3651459(963-966)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589335.3651459
Yang J(2022)Personalized Song Recommendation System Based on Vocal CharacteristicsMathematical Problems in Engineering10.1155/2022/36057282022(1-10)Online publication date: 16-Mar-2022
https://doi.org/10.1155/2022/3605728
Martyshkin AZotkina AKuzina V(2021)On the issue of profiling users of social networksContemporary information technologies10.46548/CIT-2021-0034-0014Online publication date: 20-Dec-2021
https://doi.org/10.46548/CIT-2021-0034-0014
Show More Cited By

Index Terms

Inferring personal traits from music listening history
1. Applied computing
  1. Arts and humanities
    1. Sound and music computing

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cover image ACM Conferences

MIRUM '12: Proceedings of the second international ACM workshop on Music information retrieval with user-centered and multimodal strategies

November 2012

82 pages

ISBN:9781450315913

DOI:10.1145/2390848

Program Chairs:
Cynthia C.S. Liem
Delft University of Technology, The Netherlands
,
Meinard Müller
Bonn University & MPI Informatik, Germany
,
Steven K. Tjoa
iZotope, Inc., USA
,
George Tzanetakis
University of Victoria, Canada

Copyright © 2012 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 ACM 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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SIGMM: ACM Special Interest Group on Multimedia

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 02 November 2012

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MM '12: ACM Multimedia Conference

November 2, 2012

Nara, Japan

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Cited By

Tricomi PPajola LPasa LConti MChua TNgo CKumar RLauw HKa-Wei Lee R(2024)"All of Me": Mining Users' Attributes from their Public Spotify PlaylistsCompanion Proceedings of the ACM Web Conference 202410.1145/3589335.3651459(963-966)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589335.3651459
Yang J(2022)Personalized Song Recommendation System Based on Vocal CharacteristicsMathematical Problems in Engineering10.1155/2022/36057282022(1-10)Online publication date: 16-Mar-2022
https://doi.org/10.1155/2022/3605728
Martyshkin AZotkina AKuzina V(2021)On the issue of profiling users of social networksContemporary information technologies10.46548/CIT-2021-0034-0014Online publication date: 20-Dec-2021
https://doi.org/10.46548/CIT-2021-0034-0014
Rosales AFernández-Ardèvol M(2019)Structural Ageism in Big Data ApproachesNordicom Review10.2478/nor-2019-001340:s1(51-64)Online publication date: 28-Jun-2019
https://doi.org/10.2478/nor-2019-0013
Odom WWakkary RHol JNaus BVerburg PAmram TChen ABrewster SFitzpatrick GCox AKostakos V(2019)Investigating Slowness as a Frame to Design Longer-Term Experiences with Personal DataProceedings of the 2019 CHI Conference on Human Factors in Computing Systems10.1145/3290605.3300264(1-16)Online publication date: 2-May-2019
https://dl.acm.org/doi/10.1145/3290605.3300264
Krismayer TSchedl MKnees PRabiser R(2019)Predicting user demographics from music listening informationMultimedia Tools and Applications10.1007/s11042-018-5980-y78:3(2897-2920)Online publication date: 1-Feb-2019
https://dl.acm.org/doi/10.1007/s11042-018-5980-y
Hinds JJoinson A(2018)What demographic attributes do our digital footprints reveal? A systematic reviewPLOS ONE10.1371/journal.pone.020711213:11(e0207112)Online publication date: 28-Nov-2018
https://doi.org/10.1371/journal.pone.0207112
Wang MXiao YZheng WJiao XHsu C(2018)Tag-Based Personalized Music Recommendation2018 15th International Symposium on Pervasive Systems, Algorithms and Networks (I-SPAN)10.1109/I-SPAN.2018.00040(201-208)Online publication date: Oct-2018
https://doi.org/10.1109/I-SPAN.2018.00040
Hung CKuo CHsu HChen Y(2018)Identifying Niche Singers in Online Music Streaming Services2018 IEEE 16th Intl Conf on Dependable, Autonomic and Secure Computing, 16th Intl Conf on Pervasive Intelligence and Computing, 4th Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress(DASC/PiCom/DataCom/CyberSciTech)10.1109/DASC/PiCom/DataCom/CyberSciTec.2018.00065(338-342)Online publication date: Aug-2018
https://doi.org/10.1109/DASC/PiCom/DataCom/CyberSciTec.2018.00065
Krismayer TSchedl MKnees PRabiser R(2017)Prediction of User Demographics from Music Listening HabitsProceedings of the 15th International Workshop on Content-Based Multimedia Indexing10.1145/3095713.3095722(1-7)Online publication date: 19-Jun-2017
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