Enhanced Music Recommendation Systems: A Comparative Study of Content-Based Filtering and K-Means Clustering Approaches

In the dynamic landscape of digital music services, recommendation systems play a pivotal role, evolving in tandem with advances in artificial intelligence and machine learning. This research undertakes a comparative exploration of two distinct approaches to song recommendations: content-based filtering and K-means clustering. Drawing upon an extensive Spotify dataset encompassing diverse song attributes like genre, tempo, and key, the study meticulously evaluates the efficacy of personalized track recommendations. Content-based filtering tailors recommendations to users' established preferences by scrutinizing audio features such as Danceability, Energy, and Loudness. Conversely, the K-means clustering algorithm groups’ similar songs into clusters based on shared characteristics. The primary goal is to devise a music recommendation system that impeccably aligns with user preferences. The research evaluates the performance of the K-means clustering approach using the Silhouette index as a metric, revealing a recommendation accuracy exceeding 99%. Notably, data analysis underscores the superior performance of the content-based filtering technique. These findings hold substantial importance for refining personalized music recommendation systems, offering valuable insights into the effectiveness of different methodologies in catering to user-specific musical tastes. This study contributes to the ongoing evolution of digital music services, providing a foundation for future advancements in enhancing user experience through precise and tailored music recommendations.