An Intelligent Music Recommendation System Using Machine Learning and User Preference Analysis

Abstract

The rapid expansion of digital music platforms has resulted in massive music libraries containing millions of tracks. Users often experience information overload when searching for relevant songs aligned with their interests. This research presents a comprehensive hybrid music recommendation system that integrates Content-Based Filtering, Collaborative Filtering, and Machine Learning techniques to enhance personalization, scalability, and accuracy. The proposed system utilizes song metadata, user listening behavior, interaction history, and feature embeddings to generate high-quality recommendations. Extensive experimental evaluation demonstrates that the hybrid model significantly outperforms standalone methods in terms of Precision, Recall, F1-Score, Accuracy, and Mean Average Precision. The system also addresses cold-start, sparsity, and scalability challenges effectively. Music helps us tune in to the cosmos, and the best part about music is that nothing can soothe you like a soothing melody. We chose to do this project because of all the positive aspects of music and the increasing demand for recommender systems on the market. The report comprises a topic description, and a full summary of the work completed thus far. The paper includes thorough explanations of the work completed, including snapshots of implementations, various techniques, and tools used thus far.

Introduction

Music streaming platforms provide instant access to large music libraries, but the rapid growth of content makes it difficult for users to discover songs efficiently. To address this challenge, the research proposes a scalable hybrid music recommendation system that delivers accurate and diverse song suggestions. The system handles common issues such as data sparsity and the new user (cold start) problem by combining multiple recommendation techniques.

The proposed methodology integrates content-based filtering, collaborative filtering, and hybrid modeling. Content-based filtering recommends songs based on audio features such as danceability, energy, tempo, and metadata using cosine similarity to measure similarity between feature vectors. Collaborative filtering analyzes user behavior, including listening history and ratings, using similarity measures like cosine similarity and Pearson correlation. The final recommendation is generated using a weighted hybrid formula that balances both approaches.

The system includes data collection, preprocessing (handling missing values, normalization, encoding), model training, and deployment. Advanced techniques such as matrix factorization, temporal filtering, and neural network-based feature extraction improve personalization and accuracy. The system recommends the top five most similar songs based on calculated similarity scores.

The results show that the hybrid approach enhances recommendation quality, improves user experience, and enables better music discovery. The system provides a user-friendly interface with search, playback, and personalized playlist features, demonstrating effective integration of machine learning in music recommendation.

Conclusion

The development of a music recommendation system using machine learning represents a significant advancement in how users interact with digital music platforms. Throughout this project, we have demonstrated the powerful capabilities of machine learning algorithms in understanding user behavior, predicting preferences, and delivering highly personalized music suggestions. By leveraging techniques such as collaborative filtering, content-based filtering, and hybrid models, we were able to build a system that not only learns from individual user preferences but also adapts to emerging trends and similarities across a broad user base. Collaborative filtering allowed us to capture hidden patterns and group dynamics among users, while content-based methods helped us focus on the intrinsic features of the music itself—such as genre, tempo, mood, and instrumentation.

References

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Copyright

Copyright © 2026 Kandula Manikanta, Umamaheswararao Mogili, S. Sinira, P. Bala Krishna, A. Durga Prasad, P. Poojitha, M. Ashok. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.