User preference visualization for music

Most of the music portals offer users lists of songs that are the result of black-box algorithms. The recommendation is often nontransparent for users, therefore the irrele- vant recommendation might have negative consequences. The recommendation is mainly based on the computation of similarities between users or objects. The computation relies on collaborative techniques or similarity of the contents of the objects. The purpose of this bachelor thesis is to design and implement suitable visualization of these relations in the form of an interactive graph for a certain Spotify user. The visualization should help users realize that their data have inner structures and the recommendations are based on them. The final application should also provide a music playback using the songs contained in the graph.