Research Article Open Access

Folk Music Recommendation Using NSGA-II Optimization Algorithm

Joyanta Sarkar1, Anil Rai1, Kayala Kiran Kumar2, Venkata Nagaraju Thatha3, Sowmiya Manisekaran4, Sayantan Mandal5, Joy Lal Sarkar6 and Sudeshna Das7
  • 1 Department of Humanities and Social Science, Birla Institute of Technology and Science, Pilani, India
  • 2 Freshman Engineering Department, Prasad V. Potluri Siddhartha Institute of Technology, India
  • 3 Department of Information Technology, MLR Institute of Technology, India
  • 4 Department of Artificial Intelligence and Data Science, Sri Eshwar College of Engineering, India
  • 5 School of Engineering, Amrita Vishwa Vidyapeetham, India
  • 6 Department of Computer Science and Engineering, Techno College of Engineering Agartala, India
  • 7 Department of Computer Science and Engineering, Tripura University, India

Abstract

Music recommendation systems can significantly improve the listening and search experiences of a music library or music application. There is simply too much music on the market for a user to navigate tens of millions of songs effectively. Because of the high demand for excellent music recommendations, the field of Music Recommendation Systems (MRS) is rapidly expanding. The main motivation for developing the rating-based recommendation system was to extract relevant information from user reviews of instrumental music. In this study, we suggest an NSGA-II-based music recommendation system based on user interest, popularity of an instrument, and total cost. Our aim is to maximize user interest and popularity while minimizing the costs. We also compared our method to the baseline algorithm and discovered that it outperforms the baseline approaches. We used real-world metrics like precession, recall, and F1-score to compare our method to the baseline approaches.

Journal of Computer Science
Volume 19 No. 12, 2023, 1541-1548

DOI: https://doi.org/10.3844/jcssp.2023.1541.1548

Submitted On: 17 February 2023 Published On: 8 November 2023

How to Cite: Sarkar, J., Rai, A., Kumar, K. K., Thatha, V. N., Manisekaran, S., Mandal, S., Sarkar, J. L. & Das, S. (2023). Folk Music Recommendation Using NSGA-II Optimization Algorithm. Journal of Computer Science, 19(12), 1541-1548. https://doi.org/10.3844/jcssp.2023.1541.1548

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Keywords

  • Recommendation System
  • Collaborative Filtering
  • Folk Music
  • User Ratings
  • NSGA-II