Content-Based Music Recommendation Using Non-Stationary Bayesian Reinforcement Learning
Brijgopal Bharadwaj, Ramani Selvanambi, Marimuthu Karuppiah, Ramesh Chandra Poonia · International Journal of Social Ecology and Sustainable Development · 2022
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.