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Experimenting with music taste prediction by user profiling

Published: 15 October 2004 Publication History

Abstract

In recent years many research projects have been published in the area of multimedia information retrieval (MIR). The requirement is to enable access to multimedia data with the same ease as textual information. A distinctly new branch in the MIR research area is categorizing music items by user preference. Some experiments proposed and published by different authors, showed that machine learning techniques can be applied to the problem. This work tries to extend the use of signal approximation and characterization from genre classification to recognition of user taste. The idea is to learn music preferences by applying instance based classifiers to user profiles. The audio signal (item) is characterized by features sensitive to music genres (<i>Rock, Jazz, Classical, Techno</i>). Two different classifiers are explored in order to determine the generalization accuracy of the system: k-NN and feature sub-pace based ensembles (FSSE). Feature selection techniques are explored to boost the accuracy of the predictor. The evaluation shows that this kind of problem can be solved to some extent. When the user taste is driven by a certain genre preference, the system shows reasonable accuracy

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cover image ACM Conferences
MIR '04: Proceedings of the 6th ACM SIGMM international workshop on Multimedia information retrieval
October 2004
334 pages
ISBN:1581139403
DOI:10.1145/1026711
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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Publication History

Published: 15 October 2004

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Author Tags

  1. ensemble techniques
  2. features selection
  3. music information retrieval
  4. user taste prediction

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  • (2024)Like It or Not: Exploring the Impact of (Dis)liked Background Music on Player Behavior and ExperienceProceedings of the ACM on Human-Computer Interaction10.1145/36770868:CHI PLAY(1-19)Online publication date: 15-Oct-2024
  • (2022)A Content-Based Music Recommendation System Using RapidMinerIntelligent Computing Techniques for Smart Energy Systems10.1007/978-981-19-0252-9_36(395-406)Online publication date: 14-Jun-2022
  • (2019)Social Tags and Emotions as main Features for the Next Song To Play in Automatic Playlist ContinuationAdjunct Publication of the 27th Conference on User Modeling, Adaptation and Personalization10.1145/3314183.3323455(235-239)Online publication date: 6-Jun-2019
  • (2015)Content-based music recommendation using underlying music preference structure2015 IEEE International Conference on Multimedia and Expo (ICME)10.1109/ICME.2015.7177504(1-6)Online publication date: Jun-2015
  • (2015)Review of Previous Work Related to Recommender SystemsMachine Learning Paradigms10.1007/978-3-319-19135-5_2(13-30)Online publication date: 14-Jun-2015
  • (2015)Music Recommender SystemsRecommender Systems Handbook10.1007/978-1-4899-7637-6_13(453-492)Online publication date: 2015
  • (2013)Semantic audio content-based music recommendation and visualization based on user preference examplesInformation Processing and Management: an International Journal10.1016/j.ipm.2012.06.00449:1(13-33)Online publication date: 1-Jan-2013
  • (2013)A Survey of Approaches to Designing Recommender SystemsMultimedia Services in Intelligent Environments10.1007/978-3-319-00372-6_2(7-30)Online publication date: 24-May-2013
  • (2010)The Musical AvatarProceedings of the 5th Audio Mostly Conference: A Conference on Interaction with Sound10.1145/1859799.1859813(1-8)Online publication date: 15-Sep-2010
  • (2008)MUSIPERUser Modeling and User-Adapted Interaction10.1007/s11257-007-9035-818:4(315-348)Online publication date: 1-Sep-2008
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