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Knowledge Management Techniques in Emotion-Based Music Recommendation Systems

In: Business Intelligence and Modelling

Author

Listed:
  • Catherine Marinagi

    (Agricultural University of Athens)

  • Paris Ntsounos

    (University of West Attica)

  • John Darryl Pelingo

    (University of West Attica)

  • Christos Skourlas

    (University of West Attica)

  • Anastasios Tsolakidis

    (University of West Attica)

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.

Suggested Citation

  • Catherine Marinagi & Paris Ntsounos & John Darryl Pelingo & Christos Skourlas & Anastasios Tsolakidis, 2021. "Knowledge Management Techniques in Emotion-Based Music Recommendation Systems," Springer Proceedings in Business and Economics, in: Damianos P. Sakas & Dimitrios K. Nasiopoulos & Yulia Taratuhina (ed.), Business Intelligence and Modelling, pages 415-423, Springer.
    • Handle: RePEc:spr:prbchp:978-3-030-57065-1_43
    DOI: 10.1007/978-3-030-57065-1_43
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