Music adviser emotion-driven music recommendation ecosystem

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 management engines which are smart and self managed are in demand nowadays in the m...

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Main Author: Rumiantcev, Mikhail
Other Authors: Informaatioteknologian tiedekunta, Faculty of Information Technology, Tietotekniikan laitos, Department of Mathematical Information Technology, University of Jyväskylä, Jyväskylän yliopisto
Format: Master's thesis
Language:eng
Published: 2017
Subjects:
Online Access: https://jyx.jyu.fi/handle/123456789/53196
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author Rumiantcev, Mikhail
author2 Informaatioteknologian tiedekunta Faculty of Information Technology Tietotekniikan laitos Department of Mathematical Information Technology University of Jyväskylä Jyväskylän yliopisto
author_facet Rumiantcev, Mikhail Informaatioteknologian tiedekunta Faculty of Information Technology Tietotekniikan laitos Department of Mathematical Information Technology University of Jyväskylä Jyväskylän yliopisto Rumiantcev, Mikhail Informaatioteknologian tiedekunta Faculty of Information Technology Tietotekniikan laitos Department of Mathematical Information Technology University of Jyväskylä Jyväskylän yliopisto
author_sort Rumiantcev, Mikhail
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description 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 management engines which are smart and self managed are in demand nowadays in the music industry to handle music sources amounts of which are coming towards to infinity continuously. This study demonstrates feasibility of the emotional based personalization of music recommendation system. There is still gap between human and artificial intelligence, robotics do not have intuition and emotions which represent critical point of recommendations. Taking into account significant influence of music to human emotions, we can notice that it can be a strong chain between human emotions and machines. This work provides the novel implementation of the music recommendation system based on emotional personalization, which manages human emotions by selecting and delivering music tracks based on their previous personal listening experience, collaborative and classification filtering.
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spellingShingle Rumiantcev, Mikhail Music adviser : emotion-driven music recommendation ecosystem recommendation system music web services machine learning Tietotekniikka Mathematical Information Technology 602 musiikki koneoppiminen suosittelujärjestelmät verkkopalvelut
title Music adviser : emotion-driven music recommendation ecosystem
title_alt Emotion-driven music recommendation ecosystem
title_full Music adviser : emotion-driven music recommendation ecosystem
title_fullStr Music adviser : emotion-driven music recommendation ecosystem Music adviser : emotion-driven music recommendation ecosystem
title_full_unstemmed Music adviser : emotion-driven music recommendation ecosystem Music adviser : emotion-driven music recommendation ecosystem
title_short Music adviser
title_sort music adviser emotion driven music recommendation ecosystem
title_sub emotion-driven music recommendation ecosystem
title_txtP Music adviser : emotion-driven music recommendation ecosystem
topic recommendation system music web services machine learning Tietotekniikka Mathematical Information Technology 602 musiikki koneoppiminen suosittelujärjestelmät verkkopalvelut
topic_facet 602 Mathematical Information Technology Tietotekniikka koneoppiminen machine learning music musiikki recommendation system suosittelujärjestelmät verkkopalvelut web services
url https://jyx.jyu.fi/handle/123456789/53196 http://www.urn.fi/URN:NBN:fi:jyu-201703071591
work_keys_str_mv AT rumiantcevmikhail musicadviseremotiondrivenmusicrecommendationecosystem