A Content-based System for Music Recommendation and Visualization of User PreferencesWorking on Semantic Notions
Publikation aus Digital
Ferdinand Fuhrmann, Dmitry Bogdanov and Martin Haro and Anna Xambio and Emilia Gomez and Perfecto Herrera
The amount of digital music has grown unprecedentedly during the last years and requires the development of effective methods for search and retrieval. In particular, contentbased preference elicitation for music recommendation is a challenging problem that is effectively addressed in this paper. We present a system which automatically generates recommendations and visualizes a user's musical preferences, given her/his accounts on popular online music services. Using these services, the system retrieves a set of tracks preferred by a user, and further computes a semantic description of musical preferences based on raw audio information. For the audio analysis we used the
capabilities of the Canoris API. Thereafter, the system generates music recommendations, using a semantic music similarity measure, and a user's preference visualization, mapping semantic descriptors to visual elements.