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              <text>Content-Based Music Recommendation Using Non-Stationary Bayesian Reinforcement Learning</text>
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              <text>Audio Features; Beta Sampling; Clustering; Explore-Exploit Dilemma; Independent Bandit Algorithm; Playlist Generation; Recommendation System; Thompson Sampling</text>
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              <text>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.</text>
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              <text>Bharadwaj B.; Selvanambi R.; Karuppiah M.; Poonia R.C.</text>
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              <text>International Journal of Social Ecology and Sustainable Development, Vol-13, No. 9</text>
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              <text>IGI Global</text>
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              <text>2022-01-01</text>
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              <text>&lt;a href="https://doi.org/10.4018/IJSESD.292053" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.4018/IJSESD.292053&lt;/a&gt;
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              <text>ISSN: 19478402</text>
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              <text>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</text>
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