Part 2 of the slides for the Social Tags and Music Information Retrieval Tutorial - Abstract: Social Tags are free text labels that are applied to items such as artists, playlists and songs. These tags have the potential to have a positive impact on music information retrieval research. In this tutorial we describe the state of the art in commercial and research social tagging systems for music. We explore some of the motivations for tagging. We describe the factors that affect the quantity and quality of collected tags. We present a toolkit that MIR researchers can use to harvest and process tags. We look at how tags are collected and used in current commercial and research systems. We explore some of the issues and problems that are encountered when using tags. We present current MIR-related research centered on social tags and suggest possible areas of exploration for future resear

4. Other sources of tags: Autotagging How it works Labeled Examples Unknown Examples Machine Learning Model Labeled Examples Feature Extraction Training Tagging

6. Other sources of tags: Autotagging Ground Truth for Classifiers [Craft, Wiggens, Crawford, ISMIR 2007]

7. Other sources of tags: Autotagging Ground Truth for Classifiers [Paul Lamere, JNMR 2008]

11. http://majorminer.com/search Other sources of tags: Autotagging LabROSA

12. T. Bertin-Mahiex, D. Eck, F. Maillet, and P. Lamere. Autotagger: A model for predicting social tags from acoustic features on large music databases. In Journal of New Music Research, 2008 (to appear). Other sources of tags: Autotagging BRAMS, Sun Labs

14. Other sources of tags: Autotagging BRAMS, Sun Labs

15. D. Turnbull, L. Barrington, D. Torres, and G. Lanckriet. Towards musical query-by-semantic description using the CAL500 data set. SIGIR 2007. Other sources of tags: Autotagging CAL UCSD

18. Search & Discovery

21. The Vocabulary Problem Using tag clustering to combat synonymy The 'female' cluster of a 2,000 node tag hierarchy

23. The Vocabulary Problem Using item clustering to combat polysemy

31. Discovery & Recommendation Goal: Recreate Ishkur's Guide to EDM

32. Discovery & Recommendation From Folksonomy to Taxonomy

33. Metal Discovery & Recommendation From Folksonomy to Taxonomy

34. Discovery & Recommendation Creating an Artist Hierarchy Attachment order: Artist Popularity

35. Attachment order: Artist Date Discovery & Recommendation Creating an Artist Hierarchy

36. Discovery & Recommendation Creating a Personal Artist Hierarchy

37. Discovery & Recommendation Transparency - Explaura

38. Discovery & Recommendation Transparency - Explaura

39. 2) Drag a tag to make it bigger or smaller. Discovery & Recommendation Steerable recommendations - Explaura 1) Click to add a tag or artist 3) Receive recommendations that match the tag cloud 4) Get an explanation for each recommendation

44. Attention Profile Markup Language <APML> <Profile name=&quot;music&quot;> <ImplicitData> <Concepts> <Concept key=&quot; piano &quot; value =&quot;.81&quot;/> <Concept key=&quot; female vocalists &quot; value =&quot;.72&quot;/> <Concept key=&quot; indie pop &quot; value =&quot;.65&quot;/> <Concept key=&quot; art rock &quot; value =&quot;.60&quot;/> <Concept key=&quot; experimental &quot; value =&quot;.57&quot;/> <Concept key=&quot; great lyricist &quot; value =&quot;.55&quot;/> <Concept key=&quot; noise &quot; value =&quot;.35&quot;/> </Concepts> </ImplicitData> </Profile> </APML> Turn this Into this Then this APML

45. Discovery & Recommendation Last.fm Products using Tag Data

46. Search and Discovery Last.fm Playground http://playground.last.fm/multitag

48. Last.fm Playground: Islands of Music Clustering listeners by tags

77. Acknowledgments