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2021 | OriginalPaper | Chapter

Knowledge Management Techniques in Emotion-Based Music Recommendation Systems

Authors : Catherine Marinagi, Paris Ntsounos, John Darryl Pelingo, Christos Skourlas, Anastasios Tsolakidis

Published in: Business Intelligence and Modelling

Publisher: Springer International Publishing

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Abstract

Today there is a growing interest in the combination of knowledge management techniques with the emotions, induced by music, to build Music Recommendation Systems. Advertisers can exploit music recommendations to select the type of music that can produce appropriate emotional reactions to consumers. The goal of our research is to specify an efficient and accurate way for classifying listeners’ emotions and recommend music tracks. Metadata and the emotions, induced by music, are utilized to implement a prototype of a low cost personalized Music Recommendation System. Experiments are conducted on a set of 1000 tracks with three classes of music emotions. The results indicate the classification algorithm that can better predict the emotions evoked by a song, based on associated acoustic metadata. Eventually some conclusions are given.

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Metadata
Title
Knowledge Management Techniques in Emotion-Based Music Recommendation Systems
Authors
Catherine Marinagi
Paris Ntsounos
John Darryl Pelingo
Christos Skourlas
Anastasios Tsolakidis
Copyright Year
2021
Book
Business Intelligence and Modelling

Print ISBN: 978-3-030-57064-4

Electronic ISBN: 978-3-030-57065-1

Copyright Year: 2021

DOI
https://doi.org/10.1007/978-3-030-57065-1_43

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