A Hybrid Music Recommendation System Based on K-Means Clustering and Multilayer Perceptron

Abstract

Music recommendation systems have become indispensable tools for enhancing user experiences by offering personalized playlists tailored to individual preferences. However, traditional recommendation approaches often struggle with challenges such as accurately capturing user tastes, maintaining diversity in recommendations, and addressing the cold-start problem, where limited user data hampers effective predictions. To address these issues, this study presents a hybrid recommendation model that integrates K-Means clustering and a Multilayer Perceptron (MLP) neural network to deliver coherent and diverse music recommendations. The model utilizes the all-MiniLM-L6-v2 embedding, a powerful sentence-transformer, to analyze semantic similarities in textual data such as song titles, artist names, and lyrics, encoding them into a dense vector space. Combined with normalized audio features, these embeddings enable clustering and similarity-based recommendations. Extensive experiments, conducted on datasets from Spotify and Kaggle, employed key metrics such as accuracy, F1 score, silhouette score, and cosine similarity to evaluate performance. The results highlight the system’s ability to maintain genre coherence and acoustic feature consistency, minimize track repetition, and foster user engagement. Addressing challenges like the cold-start problem and diverse user preferences, the proposed model demonstrates its potential for real-world applications. Future extensions include incorporating user feedback and supporting multi-session recommendations to adapt to evolving music trends, offering a robust and innovative approach to music recommendation systems.