Navigating Tag Space

Social tags are user-generated keywords associated with some resource on the Web. In the case of music, social tags have become an important component of "Web2.0" recommender systems, allowing users to generate playlists based on use-dependent terms such as chill or jogging that have been applied to particular songs. In our 2007 NIPS paper Automatic Generation of Social Tags for Music Recommendation (Eck, Lamere, Bertin-Mahieux, Green) we propose a method for predicting these social tags directly from MP3 files. Using a set of boosted classifiers, we map audio features onto social tags collected from the Web. The resulting automatic tags (or autotags) furnish information about music that is otherwise untagged or poorly tagged, allowing for insertion of previously unheard music into a social recommender. This avoids the ''cold-start problem'' common in such systems. Autotags can also be used to smooth the tag space from which similarities and recommendations are made by providing a set of comparable baseline tags for all tracks in a recommender system.

Here we offer a few examples of what it sounds like to navigate the resulting space of predicted social tags. Thierry applied a dimensionality reduction technique called Isomap to create a nearest neighbor graph of artists. We could then find the shortest path joining two artists. I sampling 5-second segments from songs by these artists and chained them together in an Mp3 file.



From Beethoven to The Prodigy (click graphic for full-sized image) [mp3]



From John Coltrane to metal/rock band System of a Down (click graphic for full-sized image) [mp3]



From Mozart to Nirvana (click graphic for full-sized image)[mp3]