Content-Based Music Recommendation Using Non-Stationary Bayesian Reinforcement Learning

Brijgopal Bharadwaj, Ramani Selvanambi, Marimuthu Karuppiah and Ramesh Chandra Poonia
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Brijgopal Bharadwaj: School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, India
Ramani Selvanambi: School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, India
Marimuthu Karuppiah: Department of Computer Science and Engineering, SRM Institute of Science and Technology, Delhi-NCR Campus, India
Ramesh Chandra Poonia: Department of Computer Science, CHRIST University (Deemed), India

International Journal of Social Ecology and Sustainable Development (IJSESD), 2022, vol. 13, issue 9, 1-18

Abstract: This paper presents a music recommendation system for the offline libraries of songs that employs the concepts of reinforcement learning to obtain satisfactory recommendations based on the various considered content-based parameters. In order to obtain insights about the effectiveness of the generated recommendations, parallel instances of single-play multi-arm bandit algorithms are maintained. In conjunction to this, the concepts of Bayesian learning are considered to model the user preferences, by assuming the environment’s reward generating process to be non-stationary and stochastic. The system is designed to be simple, easy to implement, and at-par with the user satisfaction, within the bounds of the input data capabilities.

Date: 2022
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