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National Repository of Grey Literature 1 records found  Search took 0.00 seconds. 
Visual Explanations in Music Recommender Systems
 Savčinský, Richard ; Peška, Ladislav (advisor) ; Petříček, Tomáš (referee)
Music recommendations from industry-leading algorithms are a product of a hybrid system combining multiple techniques. However, in the end, the user is simply left without additional information why a certain song is present in the result. One way to improve the experience for the user is to provide so-called visual explanations. For that purpose, in this thesis, we designed and proposed various forms of visual explanations for the recommended data from the Spotify API. The main goal was to highlight important hidden relationships between familiar and new music, used by Spotify but also to utilize the actual audio features for the construction of our own recommender system. We developed a modern mobile application that allows users to explore and interact with the visualizations of their own music tastes and also provides tools to customize the experience.
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