Researchers all over the world have started using Last.fm tags (which are available through our open API) in their studies. So the next time you tag something cheese on toast or woopwoop you might actually cause some researchers somewhere out there sleepless nights while they are trying to understand what kind of music those tags define ;-)
Here are some research papers that were presented at the International Conference on Music Information Retrieval in Vienna last week and that used Last.fm tags:
- Eck, Bertin-Mahieux, & Lamere (USA), Autotagging music using supervised machine learning: In this paper the authors describe their work on algorithms that try to tag music like humans. I saw a demo and the results are impressive (but I doubt they’ll ever get woopwoop and related tags right). To learn more about their work check out Lamere’s blog.
- Geleijnse, Schedl, & Knees (Netherlands & Austria), The quest for ground truth in musical artist tagging in the social web era: The authors use tags to compute the similarity of artists and classify them into genres. Geleijnse also made some excellent points on why tags used by a community of listeners are so interesting (compared to categories invented by experts who might not even like the music they are talking about).
- Hu & Downie (USA), Exploring mood metadata: Relationships with genre, artist and usage metadata: The authors use Last.fm tags to gain more insight into how mood tags are linked to other tags (e.g. “music for exercising”) and artists.
- Hu, Mert, & Downie (USA), Creating a simplified music mood classification ground-truth set: The authors used Last.fm tags to set up test sets which are used to improve algorithms that can classify moods.
- Levy & Sandler (UK), A semantic space for music derived from social tags: The authors use tags to compute music similarity and investigate important dimensions of similarity. It’s nice to see our neighbours in East London doing such interesting work :-)