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Music Playlist Recommendation Using Acoustic-Feature Transitions

Published: 20 July 2016 Publication History

Abstract

Music is important in our daily life not only for entertainment but also for mental health. When listening to music, playlists are used to eliminate the need for individual selection. The creation of playlist is difficult and tedious for users and has been the topic of research in many studies. However, many proposed playlist generation methods are based on either similar acoustic features or meta-data similarities. In this study, we propose a new method for music playlist recommendation using acoustic feature transitions where the next song will be selected such that it naturally transitions from the current song. Our preliminary evaluations show that the proposed method is more effective compared with other methods such as random selection and nearest neighbor methods

References

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P. Cano, M. Koppenberger, N. Wack, An Industrial strength content-based music recommendation system, In Proceedings of ACM SIGIR Conference '05, pages 673--673, 2005.
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J-W. Ahn, X. Amatriain, Towards fully distributed and privacy-preserving recommendation via expert collaborative filtering and restful liked data, In Proceedings of IEEE/WIC/ACM International Conference on Web Intelligence - Intelligent Agent Technology Conference (WI-IAT'10), pages 66--73, 2010.
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K. Tada, R. Yamanishi, S. Kato, Interactive Music Recommendation System for Adapting Personal Affection, In Proceedings of International Conference on Entertainment Computing, Lecture Notes in Computer Science (LNCS), Vol. 7522, pages 417--510, Springer, 2012.
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A. Flexer, D. Schnitzer, M. Gasser, G. Widmer, Playlist Generation using Start and End SongsïijŇIn Proceedings of ISMIR Conference '08, pages 173--178, 2008.
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S. Pauws, B. Eggen, PATS: Realization and User Evaluation of an Automatic Playlist Generator, In Proceedings of ISMIR Conference '02, pages 222--230, 2002.
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B. Logan, Music Recommendation from Song Sets, In Proceedings of ISMIR Conference '04, pp.425-428, 2004.
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Cited By

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  • (2022)An automated system recommending background music to listen to while workingUser Modeling and User-Adapted Interaction10.1007/s11257-022-09325-y32:3(355-388)Online publication date: 18-May-2022
  • (2018)Attentive neural architecture incorporating song features for music recommendationProceedings of the 12th ACM Conference on Recommender Systems10.1145/3240323.3240397(417-421)Online publication date: 27-Sep-2018
  • (2018)Music Playlist Recommender System AFT-ISProceedings of the 2018 10th International Conference on Computer and Automation Engineering10.1145/3192975.3193019(58-61)Online publication date: 24-Feb-2018
  • Show More Cited By

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  1. Music Playlist Recommendation Using Acoustic-Feature Transitions

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        cover image ACM Other conferences
        C3S2E '16: Proceedings of the Ninth International C* Conference on Computer Science & Software Engineering
        July 2016
        152 pages
        ISBN:9781450340755
        DOI:10.1145/2948992
        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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        Association for Computing Machinery

        New York, NY, United States

        Publication History

        Published: 20 July 2016

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        Author Tags

        1. Acoustic-features
        2. Music
        3. Playlist
        4. Recommendation
        5. Transition

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        • Short-paper
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        C3S2E '16

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        Overall Acceptance Rate 12 of 42 submissions, 29%

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        Cited By

        View all
        • (2022)An automated system recommending background music to listen to while workingUser Modeling and User-Adapted Interaction10.1007/s11257-022-09325-y32:3(355-388)Online publication date: 18-May-2022
        • (2018)Attentive neural architecture incorporating song features for music recommendationProceedings of the 12th ACM Conference on Recommender Systems10.1145/3240323.3240397(417-421)Online publication date: 27-Sep-2018
        • (2018)Music Playlist Recommender System AFT-ISProceedings of the 2018 10th International Conference on Computer and Automation Engineering10.1145/3192975.3193019(58-61)Online publication date: 24-Feb-2018
        • (2018)FocusMusicRecommenderProceedings of the 23rd International Conference on Intelligent User Interfaces10.1145/3172944.3172981(7-17)Online publication date: 5-Mar-2018
        • (2017)Music Playlist Recommendation Using Acoustic-Feature Transition Inside the SongsProceedings of the 15th International Conference on Advances in Mobile Computing & Multimedia10.1145/3151848.3151880(216-219)Online publication date: 4-Dec-2017
        • (2017)Characterizing User Behavior in a Music Navigation Application with Real-time FeedbackProceedings of the 23rd Brazillian Symposium on Multimedia and the Web10.1145/3126858.3126875(133-140)Online publication date: 17-Oct-2017
        • (2017)Analysis of Music Transition in Acoustic Feature Space for Music RecommendationProceedings of the 9th International Conference on Machine Learning and Computing10.1145/3055635.3056602(77-80)Online publication date: 24-Feb-2017

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