| Yhteenveto: | 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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