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From User Context to Tailored Playlists: A User Centered Approach to Improve Music Recommendation System

Published: 24 January 2024 Publication History

Abstract

Systems for recommending music have become a popular tool for delivering track suggestions to users that align with their unique listening habits or preferences. Despite their ubiquity, a recurring focus in these systems has been the accuracy of predictions, often bypassing the impact of user experience (UX) while generating recommendations. The so-called cold-start problem, where the system encounters new users without sufficient data about them, has remained a persistent challenge. This research introduces an approach based on user experience and other aspects that may influence music recommendation (e.g., the cold-start problem, feedback, cognitive effort). The approach, designed for integration into a mobile app, emphasizes context by pre-analyzing user-created playlists for their current context. Our investigation employed the Intermediate Semiotic Inspection Method (ISIM) to evaluate the system’s communicability. This method allowed us to spotlight three categories essential to music recommendation systems: innovative suggestions, constant updates, and users’ engagement in rating. We also apply the Technology Acceptance Model (TAM) to evaluate the system’s acceptance. Our work showcases how a semiotic inspection approach, combined with a focus on user context and feedback, can drastically enhance user experience in music recommendation systems.

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  • (2025)Aggregating Contextual Information for Multi-Criteria Online Music RecommendationsIEEE Access10.1109/ACCESS.2025.352751213(8790-8805)Online publication date: 2025

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    IHC '23: Proceedings of the XXII Brazilian Symposium on Human Factors in Computing Systems
    October 2023
    791 pages
    ISBN:9798400717154
    DOI:10.1145/3638067
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    Publication History

    Published: 24 January 2024

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    Author Tags

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

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    • Fundação Coordenação de Aperfeiçoamento de Pessoal de Nível Superior

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    • (2025)Aggregating Contextual Information for Multi-Criteria Online Music RecommendationsIEEE Access10.1109/ACCESS.2025.352751213(8790-8805)Online publication date: 2025

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