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

Title

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

Creator

Bharadwaj B.; Selvanambi R.; Karuppiah M.; Poonia R.C.

Description

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 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 on-par with user satisfaction within the bounds of the input data capabilities. Copyright 2021, IGI Global.

Source

International Journal of Social Ecology and Sustainable Development, Vol-13, No. 9

Date

2022-01-01

Publisher

IGI Global

Subject

Audio Features; Beta Sampling; Clustering; Explore-Exploit Dilemma; Independent Bandit Algorithm; Playlist Generation; Recommendation System; Thompson Sampling

Coverage

Bharadwaj B., School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, India; Selvanambi R., School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, India; Karuppiah M., Department of Computer Science and Engineering, SRM Institute of Science and Technology, Delhi-NCR Campus, India; Poonia R.C., Department of Computer Science, CHRIST University (Deemed), India

Rights

Restricted Access

Relation

ISSN: 19478402

Format

Online

Language

English

Type

Article

Identifier

https://doi.org/10.4018/IJSESD.292053

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85146645850&doi=10.4018%2fIJSESD.292053&partnerID=40&md5=d89dc0c1031d0b1e9bcd03fab916b848