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Managing Cold-Start Issues in Music Recommendation Systems: An Approach Based on User Experience

Published: 27 June 2023 Publication History

Abstract

Music recommendation systems have been widely used to suggest songs to users based on their listening history or interests. Traditionally, most recommender systems have focused on prediction accuracy without considering user experience (UX) in generating recommendations. In addition, there is also the problem of cold-start, which is when the system has new users and not enough data is available about them. This study presents a new approach for music recommendation based on user experience that explores the cold-start problem. We implemented our approach in a mobile application and evaluated the system’s communicability using the Intermediate Semiotic Inspection Method (ISIM). As a result, we identified three categories relevant to music recommendation systems: novelty in recommendations, continuous updates, and users’ interest in rating. In addition, we checked each participant’s understanding of the tool, which was generally very close to the intended proposal.

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Cited By

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  • (2024)Match Musical: Avaliando a UX de Recursos Colaborativos no SpotifyAnais do XIX Simpósio Brasileiro de Sistemas Colaborativos (SBSC 2024)10.5753/sbsc.2024.238074(98-111)Online publication date: 29-Apr-2024
  • (2024)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: 24-Jan-2024
  • (2024)Beyond the Trends: Evolution and Future Directions in Music Recommender Systems ResearchIEEE Access10.1109/ACCESS.2024.338668412(51500-51522)Online publication date: 2024

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    cover image ACM Conferences
    EICS '23 Companion: Companion Proceedings of the 2023 ACM SIGCHI Symposium on Engineering Interactive Computing Systems
    June 2023
    104 pages
    ISBN:9798400702068
    DOI:10.1145/3596454
    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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    Published: 27 June 2023

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    Author Tags

    1. Music recommendation
    2. context
    3. evaluation.
    4. user experience

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    View all
    • (2024)Match Musical: Avaliando a UX de Recursos Colaborativos no SpotifyAnais do XIX Simpósio Brasileiro de Sistemas Colaborativos (SBSC 2024)10.5753/sbsc.2024.238074(98-111)Online publication date: 29-Apr-2024
    • (2024)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: 24-Jan-2024
    • (2024)Beyond the Trends: Evolution and Future Directions in Music Recommender Systems ResearchIEEE Access10.1109/ACCESS.2024.338668412(51500-51522)Online publication date: 2024

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