Title

Comparative Analysis of Content-Based Recommender Systems Using Distance Metrics and Feature Sets for Classical Music

Date of Award

2019

Document Type

Thesis

Degree Name

Master of Science in Chemistry, Straight Program

Department

Information Systems & Computer Science

First Advisor

Andrei D. Coronel, PhD

Abstract

Music recommender systems have become a popular tool utilized by numerous online music streaming apps like Spotify and Apple Music. De- spite the prevalence of music recommenders, not many have created one particularly for classical music. Although listeners of classical music are not typically dominant, they still constitute as a significant target group for music recommender systems. Classical music will greatly benefit from the use of a content-based recommendation system that will analyze the music’s rhythmic, melodical, and chordal features as these features help define a user’s musical taste. As such, we present an approach for content-based recommendation using similarity of classical music using high-level musical features. The chosen high-level musical features include rhythmic variability, chromatic motion, melodic embellishments, and key. Comparison of vari- ous feature selection and processing is used and experimented on to min- imize the computational expense of the method while maximizing results. Finally, the paper compares different evaluations metrics that represent the effectiveness of the recommendations through a listening test. The results demonstrate the feasibility of these features and techniques in creating a content-based recommender for classical music.

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