TY  - JOUR
AU  - Sarkar, Joyanta 
AU  - Rai, Anil 
AU  - Kumar, Kayala Kiran 
AU  - Thatha, Venkata Nagaraju 
AU  - Manisekaran, Sowmiya 
AU  - Mandal, Sayantan 
AU  - Sarkar, Joy Lal 
AU  - Das, Sudeshna 
PY  - 2023
TI  - Folk Music Recommendation Using NSGA-II Optimization Algorithm
JF  - Journal of Computer Science
VL  - 19
IS  - 12
DO  - 10.3844/jcssp.2023.1541.1548
UR  - https://thescipub.com/abstract/jcssp.2023.1541.1548
AB  - 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.