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<p>Prior research has highlighted the need to balance accuracy with other exploration-oriented system objectives, such as novelty, serendipity, and diversity. However, relatively little has been done to investigate how real users perceive and experience this trade-off in music recommender systems. To address this gap, we conducted a user experiment study in which participants interacted with two contrasting music discovery tools: a seed-based discovery interface designed for accuracy, and Spotify’s Discover Weekly, which prioritizes exploration. One hundred and forty-four Spotify users completed the study in which they were asked to evaluate tracks/playlists resulting from the two music discovery tools. The results indicated that Discover Weekly performed significantly better in exploration-related measures, including diversity, novelty, and serendipity, and fewer previously known tracks. However, Discover Weekly performed significantly worse in accuracy, as measured by average track rating. In addition, overall satisfaction with the playlist is significantly influenced by both the perceived diversity of the playlist and the number of known tracks. To better understand individual differences in navigating this trade-off, we examined four music preference characteristics: music involvement, music self-identity, preference for diversity, and openness to novelty. These traits, measured via a questionnaire, allowed us to investigate their roles in moderating the effectiveness of these tools. Music involvement and music self-identity were positively associated with track ratings, regardless of the tool used. Importantly, individuals who consider music central to their self-identity demonstrated greater appreciation for the recommendations provided by Discover Weekly. Structural equation modeling further showed that music self-identity moderated users’ evaluations of Discover Weekly, such that individuals with higher music self-identity reported greater satisfaction with its recommendations, partly by attenuating the negative impact of reduced familiarity on perceived accuracy. Our findings highlight the need to balance familiarity and novelty and the importance of incorporating psychological factors into personalization strategies to enhance user satisfaction.</p>

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Balancing exploitation and exploration: how preference characteristics influence users' evaluation of Spotify's Discover Weekly

  • 期刊论文

  • 发表日期：

    2026年6月27日

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  • Muh-Chyun Tang

摘要

Prior research has highlighted the need to balance accuracy with other exploration-oriented system objectives, such as novelty, serendipity, and diversity. However, relatively little has been done to investigate how real users perceive and experience this trade-off in music recommender systems. To address this gap, we conducted a user experiment study in which participants interacted with two contrasting music discovery tools: a seed-based discovery interface designed for accuracy, and Spotify’s Discover Weekly, which prioritizes exploration. One hundred and forty-four Spotify users completed the study in which they were asked to evaluate tracks/playlists resulting from the two music discovery tools. The results indicated that Discover Weekly performed significantly better in exploration-related measures, including diversity, novelty, and serendipity, and fewer previously known tracks. However, Discover Weekly performed significantly worse in accuracy, as measured by average track rating. In addition, overall satisfaction with the playlist is significantly influenced by both the perceived diversity of the playlist and the number of known tracks. To better understand individual differences in navigating this trade-off, we examined four music preference characteristics: music involvement, music self-identity, preference for diversity, and openness to novelty. These traits, measured via a questionnaire, allowed us to investigate their roles in moderating the effectiveness of these tools. Music involvement and music self-identity were positively associated with track ratings, regardless of the tool used. Importantly, individuals who consider music central to their self-identity demonstrated greater appreciation for the recommendations provided by Discover Weekly. Structural equation modeling further showed that music self-identity moderated users’ evaluations of Discover Weekly, such that individuals with higher music self-identity reported greater satisfaction with its recommendations, partly by attenuating the negative impact of reduced familiarity on perceived accuracy. Our findings highlight the need to balance familiarity and novelty and the importance of incorporating psychological factors into personalization strategies to enhance user satisfaction.