Music Recommendation System Using Machine Learning

Abstract

Music recommendation systems function as personalized assistants that analyze listener preferences and suggest relevant songs or playlists. These systems utilize past user data to generate recommendations that align with individual tastes. However, users often struggle to identify the most suitable songs due to the vast availability of music content. Various techniques have been employed to enhance recommendation accuracy, including collaborative filtering, content-based filtering, and hybrid models. Initially, the system gathers substantial user data, such as listening history and ratings, to create comprehensive listener profiles. Several machine learning algorithms, such as Cosine Similarity, K-Nearest Neighbors (KNN), and the Weighted Product Method, can be implemented for effective recommendations. Advanced hybrid approaches, integrating Singular Value Decomposition and Factorization Machines, further optimize recommendation accuracy. These systems are a specialized application of machine learning, leveraging diverse techniques to analyze user behavior and deliver personalized music recommendations. This paper presents a Music Recommendation System that incorporates multiple advanced technologies to improve accuracy and user experience

Introduction

1. Thyroid Disease Prediction

A key challenge in diagnosing thyroid disorders lies in accurately analyzing complex datasets. Machine learning (ML) techniques can reduce the number of features required while maintaining high predictive accuracy.

• Dataset: Sourced from the UCI Machine Learning Repository.

• Algorithms Used: Naive Bayes, Decision Tree, Random Forest, KNN, and Logistic Regression.

• Tools: PyCaret was used for model implementation and evaluation.

• Findings:

  • Naive Bayes achieved the highest accuracy (95.91%).

  • Logistic Regression (One-vs-Rest and Multinomial) also performed well (85–86% accuracy).

• Goal: Efficiently classify thyroid conditions (hyperthyroid, euthyroid, hypothyroid, and sick) using fewer features with high accuracy.

2. Music Recommendation System

With the growth of digital music platforms, personalized recommendation systems are essential to help users navigate large song libraries.

System Overview

• Technologies Used:

  • Frontend: Streamlit for user-friendly UI.

  • Backend: MongoDB for storing user preferences, song metadata, and feedback.

  • APIs: Spotify API for real-time song details like tempo, mood, popularity.

• Dataset: A collection of Hindi songs with attributes like artist, mood, tempo, and popularity.

Recommendation Methods

1. Content-Based Filtering: Recommends songs based on audio features (e.g., mood, tempo, genre) of previously liked tracks using cosine similarity.

2. Collaborative Filtering:

   • User-based: Finds similar users and recommends their liked songs.

   • Item-based: Suggests songs often liked together.

3. Hybrid Filtering: Combines both content and collaborative methods to handle cold-start problems and improve recommendation accuracy.

Features

• Authentication: Each user has a personalized profile.

• Feedback System: Users can rate and review songs, improving future recommendations.

• Machine Learning: Enables real-time, adaptive suggestions based on usage patterns.

Implementation

• Languages & Tools: Python, Pandas, PyMongo, Streamlit.

• Libraries Used: CountVectorizer, Cosine Similarity for measuring song similarity.

Literature Review Highlights

• Emotion-based systems using facial expressions and CRNNs.

• Hybrid ensemble models requiring large data for performance.

• MCRS combining KNN and WPM with high usability scores.

• Audio embeddings via Siamese networks for better personalization.

Conclusion

This paper presents a Music Recommendation System that integrates multiple components: a frontend interface built using Streamlit to ensure an interactive and user-friendly experience; a backend powered by MongoDB, which stores song metadata, user preferences, and feedback, enabling real-time updates and personalized recommendations; and the Spotify API, which provides dynamic song details such as artist information, album artwork, tempo, popularity, and mood, enhancing the accuracy and relevance of music suggestions.Future work includes implementing machine learning-based collaborative filtering to provide more personalized recommendations.

References

[1] Adiyansjah, A S Gunawan, A., &Suhartono, D., “Music Recommender System Based on Genre using Convolutional Recurrent Neural Networks”, 4th International Conference on Computer Science and Computational Intelligence 2019 (pp. 99-109), Elsevier,2019. [2] Chen, K., Liang, B., Ma, X., &Gu, M.,“Learning Audio Embeddings with User Listening Data for Content-based Music Recommendation”, IEEE International Conference on Acoustics, Speech and Signal Processing, IEEE, 2021. [3] Joel, J. S., Thompson, B. E., Thomas, S. R., Kumar, T. R., Prince, S., &Bini, D.,“Emotion based Music Recommendation System using Deep Learning Model”, International Conference on Inventive Computation Technologies (ICICT),Lalitpur, Nepal, IEEE, 2023. [4] Markus , S., Knees, P., McFee, B., &Bogdanov, D., “Music Recommendation Systems: Techniques, Use Cases, and Challenges”, In F. Ricci, Beer-Sheva, & B. Shapira, Recommender Systems Handbook (pp. 927–971), New York, Springer, New York, NY. (2021). [5] Moghal , K. , “Music Recommendation System Using Machine Learning”, International Journal of Research Publication and Reviews , 21-24. [6] Moharekar, D., Vadar, M., Pol, D., Bhaskar, D., &Moharekar, M. , “Thyroid Disease Detection Using Machine Learning and Pycaret”, SpecialusisUgdymas / Special Education , 10150-10160, 2022 . [7] Tyagi, M., Ali, M., Kaim, G., Lamba, T., &Chugh, G., “Music Recommendation System Using Multiple Machine Learning Models”, International Conference on Innovative Computing & Communication (ICICC) 2024, Elsevier, 2023. [8] Vadar, P., Pol, U., &Moharekar, T., “Machine Learning Techniques for the Classification of Thyroid Disease”, International Journal for Research in Applied Science & Engineering Technology (IJRASET), 375-380, 2024. [9] Verma, V., Marathe, N., Sanghavi, P., &Nitnaware, D., “Music Recommendation System Using Machine Learning”, International Journal of Scientific Research in Computer Science Engineering and Information Technology, 80-88, (2021). [10] Yenkikar, A., Mirajkar, R., Ahire, P., Shinde, P., &Patil, D., “Machine Learning-based Music Recommendation System Based on User Interest”, 2nd World Conference on Communication & Computing, Raipur: IEEE. ,2024.

Copyright

Copyright © 2025 Prajyot P. Bhopale , Adesh R. Hande , Urmila R. Pol , Parashuram S. Vadar, Tejashree T. Moharekar. 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.