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tagging, communities, vocabulary, evolution

tagging, communities, vocabulary, evolution. Shilad Sen S. Lam, D. Cosley, A. Rashid, D. Frankowski, F. Harper, J. Osterhouse, J. Riedl GroupLens Research University of Minnesota Dept. of Computer Science. from flickr.com:. tags: dayton, graffiti, ohio, spray_paint, tag.

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tagging, communities, vocabulary, evolution

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  1. tagging, communities,vocabulary, evolution Shilad Sen S. Lam, D. Cosley, A. Rashid, D. Frankowski, F. Harper, J. Osterhouse, J. Riedl GroupLens Research University of Minnesota Dept. of Computer Science

  2. from flickr.com: tags: dayton, graffiti, ohio, spray_paint, tag Tagging Background • A tag is a free-form word or phrase used to describe an object • Although users may tag for selfish reasons, other people benefit from their tags • Tag-based information can be more flexible, intuitive and scalable than traditional ontologies

  3. Example Vocabulary #1 FACTUAL

  4. Example Vocabulary #2 SUBJECTIVE

  5. Example Vocabulary #3 PERSONAL

  6. Research Questions • How do tagging vocabularies evolve? • Can we control vocabulary evolution? • Are specific types of vocabularies more or less valuable to the community?

  7. MovieLens • movielens.org • Started ~1995 • Users rate movies ½ to 5 stars • Users get recommendations

  8. MovieLens Tagging Features Tagging Intro Page description call-to-tag

  9. MovieLens Tagging Features Movie Lists

  10. MovieLens Tagging Features Adding Tags

  11. Experimental Data • 29 days • 3366 users • 18.8% of users tagged at least one movie • 3,623 unique tag phrases…. • Resulting in 11,433 tag applications tag application = <user, movie, tag phrase>

  12. Tag Classes • Factual • Top: action, drama, comedy, disney, teen, james bond • 64% of tag applications • Subjective • Top: classic, chick flick, funny, overrated, girlie movie • 22% of tag applications • Personal • Top: bibliothek, in netflix queue, settled, dvd, my dvds • 11% of tag applications

  13. Research Questions • How do tagging vocabularies evolve? • Can we control vocabulary evolution? • Are specific types of vocabularies more or less valuable to the community?

  14. How do Vocabularies Evolve? We consider two factors in tagging behavior: • Investment and habit – People tag similarly to how they have tagged in the past. • Community Influence – People tag similarly to how they see other people tag. (more complex model in paper)

  15. Tag Class Similarities

  16. Research Questions • How do tagging vocabularies evolve? • Can we control vocabulary evolution? • Are specific types of vocabularies more or less valuable to the community?

  17. Can We Affect Vocabulary? • Four experimental groups • Each group’s set of tags were maintained separately • Experimental groups use different tag selection algorithms to show community tags for a movie.

  18. Unshared Selection Algorithm Tags for Pulp Fiction in the unshared group: applied tags classic film interesting new way of film bibliothek NO community tags displayed

  19. Shared Selection Algorithm Tags for Pulp Fiction in the shared group: buddy movie, Bruce Willis, classic (2), great movie, John Travolta, need to buy, tarantino (2), tarantino is god (3), this is the weirdest movie I ever liked applied tags displayed community tags buddy movie, tarantino, this is the weirdest movie I ever liked

  20. Shared-Pop Selection Algorithm Tags for Pulp Fiction in the shared-pop group: 90s, crime (2), dialogue, drama (2), hilarity, hit men (2), John Travolta (2), murder, Quentin Tarantino (4), Samuel L., Samuel L. Jackson, ultra-violence, USA applied tags displayed community tags Quentin Tarantino (4), drama (2), hit men (2)

  21. Shared-Rec Selection Algorithm Tags for Pulp Fiction in the shared-rec group: Pulp Fiction Fight Club Reservoir Dogs The Godfather action, comedy, dvd (2), dvd-r, great, mob, off-beat, quirky (2), surreal, tarantino (7), theater, vhs surreal surreal tarantino dvd applied tags displayed community tags tarantino, dvd, surreal

  22. factual subjective personal Vocabularies Evolve Differently! Relative tag class proportions over time: unshared shared shared-rec shared-pop

  23. Research Questions • How do tagging vocabularies evolve? • Can we control vocabulary evolution? • Are specific types of vocabularies more or less valuable to the community?

  24. Vocabulary Value Post-experiment user survey (365 users) • Asked general questions about tagging • I create my own tags to have fun. (agree/disagree) • Overall, I think the tagging features help me express my opinions. • etc… • Asked users about specific applications of tags to movies: • Overall I would like to see the tag “phone booth” for the The Matrix. • I think the tag “ultra-violence” helps me decide whether or not to watch Pulp Fiction. • etc…

  25. Value of Factual Tags: Regarding the tag “anime” for “Spirited Away”: This is a great tag, because it has no bias associated with it, it merely gives more information about the genre of the movie. Great tag.. • Taggers agreed that factual tags are: • Useful overall (56% agreement) • Useful for learning about movies (60%) • Useful for finding movies (59%)

  26. Value of Subjective Tags: Regarding the tag “surreal” for “Eternal Sunshine of the Spotless Mind” …”surreal” is appropriate. and from another user… …somebody saying a movie is "funny" or "surreal" doesn't tell me anything - because I don't know whether their tastes are similar to mine. • 44% agreement subjective tags were useful, but… • 87% agreement users’ own subjective tags helped express their opinions.

  27. Value of Personal Tags: Regarding the tag “My DVDs” for “Dawn of the Dead”: The person who created this tag was either clueless that the tag would be shared or didn't care. Either way, how does it help a single other person on the planet to know that this film is in his DVD collection? • 17% agreement that personal tags were useful, but.. • 87% agreement that personal tags helpful in organizing for the taggers themselves

  28. Conclusion Post experiment usage: • 33,238 tag applications by 1759 users Recap: • Users tag similarly to: • How they have tagged in the past • The community tags they have seen • Vocabulary formation is a “volatile” process • Different tag classes have very different utilities

  29. Open Questions / Future Work • Are the tag-class differences between experimental groups deterministic? • Would more complex tagging models better capture tagging behavior? • How can a system distinguish between “good” tags and “bad” tags?

  30. Thank You! • John Riedl • GroupLens research • National Science Foundation • (IIS 03-24851 and IIS 05-34420) • MovieLens users! contact me at shilad@gmail.com

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