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

Abstract

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 Kmeans 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 Kmeans 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.