Emotion Based Music Recommendation System
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
Date
2020Copyright
© Authors, 2020
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.
...
Publisher
Fruct OyParent publication ISBN
978-952-69244-2-7Conference
Conference of Open Innovations Association FRUCTIs part of publication
FRUCT '26 : Proceedings of the 26th Conference of Open Innovations Association FRUCT, Yaroslavl, Russia, 23-25 April 2020ISSN Search the Publication Forum
2305-7254
Original source
https://fruct.org/publications/acm26/files/Rum.pdfPublication in research information system
https://converis.jyu.fi/converis/portal/detail/Publication/35358227
Metadata
Show full item recordCollections
License
Related items
Showing items with similar title or keywords.
-
Exploring value in eCommerce artificial intelligence and recommendation systems
Änäkkälä, Tuomas (2021)Tekoälyn päämääränä on saavuttaa järjestelmä, joka jäljittelee ihmisen luonnollista älykkyyttä. Suosittelujärjestelmä on tieteenala sekä tekoälyä hyödyntävä järjestelmä. Suosittelujärjestelmä tarjoaa käyttäjilleen personoitua ... -
Music adviser : emotion-driven music recommendation ecosystem
Rumiantcev, Mikhail (2017)In respect of the big amounts of music available in the web, people met the problem of choice. From another side, practically unlimited resources can bring us new opportunities in the music context. Efficient data ... -
Emotions and Activity Recognition System Using Wearable Device Sensors
Rumiantcev, Mikhail (FRUCT Oy, 2021)Nowadays machines have become extremely smart, there are a lot of existing services that seemed to be unexpectable and futuristic decades or even a few years ago. However, artificial intelligence is still far from human ... -
When more is less : the other side of artificial intelligence recommendation
Chen, Sihua; Qiu, Han; Zhao, Shifei; Han, Yuyu; He, Wei; Siponen, Mikko; Mou, Jian; Xiao, Hua (Elsevier; China Science Publishing & Media Ltd., 2022)Based on consumers' preferences, AI (artificial intelligence) recommendation automatically filters information, which provokes scholars' debate. Supporters believe that by analyzing the consumers' preferences, AI recommendation ... -
Recommending Serendipitous Items using Transfer Learning
Pandey, Gaurav; Kotkov, Denis; Semenov, Alexander (ACM Press, 2018)Most recommender algorithms are designed to suggest relevant items, but suggesting these items does not always result in user satisfaction. Therefore, the efforts in recommender systems recently shifted towards serendipity, ...