Folk Music Recommendation Using NSGA-II Optimization Algorithm
- 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.
DOI: https://doi.org/10.3844/jcssp.2023.1541.1548
Copyright: © 2023 Joyanta Sarkar, Anil Rai, Kayala Kiran Kumar, Venkata Nagaraju Thatha, Sowmiya Manisekaran, Sayantan Mandal, Joy Lal Sarkar and Sudeshna Das. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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Keywords
- Recommendation System
- Collaborative Filtering
- Folk Music
- User Ratings
- NSGA-II