A4 Article in conference proceedings
Emotion Based Music Recommendation System (2020)

Rumiantcev, M., & Khriyenko, O. (2020). Emotion Based Music Recommendation System. In S. Balandin, I. Paramonov, & T. Tyutina (Eds.), FRUCT '26 : Proceedings of the 26th Conference of Open Innovations Association FRUCT, Yaroslavl, Russia, 23-25 April 2020 (pp. 639-645). Fruct Oy. Proceedings of Conference of Open Innovations Association FRUCT. https://fruct.org/publications/acm26/files/Rum.pdf

JYU authors or editors

Publication details

All authors or editors: Rumiantcev, Mikhail; Khriyenko, Oleksiy

Parent publication: FRUCT '26 : Proceedings of the 26th Conference of Open Innovations Association FRUCT, Yaroslavl, Russia, 23-25 April 2020

Parent publication editors: Balandin, S.; Paramonov, I.; Tyutina, T.

Place and date of conference: Yaroslavl, Russia, 23.-24.4.2020

ISBN: 978-952-69244-2-7

Journal or series: Proceedings of Conference of Open Innovations Association FRUCT

ISSN: 2305-7254

eISSN: 2343-0737

Publication year: 2020

Pages range: 639-645

Publisher: Fruct Oy

Publication country: Finland

Publication language: English

Persistent website address: https://fruct.org/publications/acm26/files/Rum.pdf

Publication open access: Openly available

Publication channel open access: Open Access channel

Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/68883

Abstract

Nowadays, music platforms provide easy access to large amounts of music. They are working continuously to improve music organization and search management thereby addressing the problem of choice and simplify exploring new music pieces. Recommendation systems gain more and more popularity and help people to select appropriate music for all occasions. However, there is still a gap in personalization and emotions driven recommendations. Music has a great influence on humans and is widely used for relaxing, mood regulation, destruction from stress and diseases, to maintain mental and physical work. There is a wide range of clinical settings and practices in music therapy for wellbeing support. This paper will present the design of the personalized music recommendation system, driven by listener feelings, emotions and activity contexts. With a combination of artificial intelligence technologies and generalized music therapy approaches, a recommendation system is targeted to help people with music selection for different life situations and maintain their mental and physical conditions.

Keywords: music; recommendations; recommender systems; emotions; artificial intelligence; machine learning

Contributing organizations

Ministry reporting: Yes

Reporting Year: 2020

JUFO rating: 1