@article {10.3844/jcssp.2023.1541.1548,
article_type = {journal},
title = {Folk Music Recommendation Using NSGA-II Optimization Algorithm},
author = {Sarkar, Joyanta and Rai, Anil and Kumar, Kayala Kiran and Thatha, Venkata Nagaraju and Manisekaran, Sowmiya and Mandal, Sayantan and Sarkar, Joy Lal and Das, Sudeshna},
volume = {19},
number = {12},
year = {2023},
month = {Nov},
pages = {1541-1548},
doi = {10.3844/jcssp.2023.1541.1548},
url = {https://thescipub.com/abstract/jcssp.2023.1541.1548},
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 = {Journal of Computer Science},
publisher = {Science Publications}
}