Abstract
Abstract. Spotify, one of the largest music streaming service providers, boasts a vast user base and an extensive music catalog. To gain a deeper understanding of Spotify users' music preferences and behavioral patterns, this paper conducts a thorough analysis and explores the correlation between music features and user behavior. It collects Spotify users' music listening data and extracts various music features such as energy, danceability, valence, and beats per minute (BPM). These features not only reflect the musical style and rhythm but also potentially correlate with users' preferences and listening habits. Then we employ two machine learning models, Decision Trees and Random Forests, to model and analyze Spotify users' behavior data. Through these models, we can accurately identify potential relationships between music features and user behavior. The experimental results indicate that music features such as energy, danceability, valence, and BPM have a significant impact on users' music selection and preferences. By analyzing the most popular songs on Spotify over different time periods, we discover that songs with high energy, danceability, and valence tend to be more popular among users. This finding not only validates the correlation between music features and user behavior but also reveals the user preferences and market demands behind music trends. For music streaming service providers, understanding users' music preferences and behavioral patterns is crucial. By deeply analyzing user data, music streaming services can better satisfy users' needs, enhance user experience, and stand out in the fiercely competitive market. These findings have important practical implications for music streaming service providers and provide new insights and methodologies for the research and development of the music industry.
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