Analyzing the Characteristics of Music Playlists using Song Lyrics and Content-based Features

Thesis Type Master
Thesis Status
Currently running
Student Stefan Wurzinger
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Research Field

Today's online music platforms offer you the ability to listen to personalized playlists or radio stations. The collaborative filtering (CF) technique is widely used to offer such services. A well-known disadvantage of CF is that new and unpopular songs can't be recommended (cold-start problem). To mitigate the cold-start problem, song lyrics and content-based features can be considered when recommending music. Therefore, the goal of this thesis is to analyze the characteristics of music playlists to find some significant lyrics and content-based features which can be used to decide if a song belongs to a specific playlist or not. Furthermore, it should be investigated if the extracted features can be used for music recommendation or automatic playlist generation.