1. Thoughts on Tagging & Search Marti Hearst UC Berkeley

2. Marti Hearst, Future of Search ‘07 Talk Outline: Two Main Points 1.Massive user behavior is aiding search algorithms in interesting ways. 2.Going deeper: An examination of social tagging:  The controversy  Research questions  Our work on automating creation of metadata structure

3. Marti Hearst, Future of Search ‘07 User-contributed content is exploding!

4. Marti Hearst, Future of Search ‘07 Social Information & Search  Trend: human behavioral information is getting “baked in” to search algorithms.  In many cases, the actions of the many is more useful than the actions of the individual.  Three examples follow.

5. Marti Hearst, Future of Search ‘07 Actions of the Many vs. Individual 1.Anchor text for improved ranking.  vs author-supplied meta-tags

6. Marti Hearst, Future of Search ‘07 Actions of the Many vs. Individual 2.“Clickthrough” to improve ranking.  vs. an individual’s prior clicks  Joachims et al. and Agichtein et al. found that human selections of links from search results could improve rankings for popular queries.  Some surprising rules:  Assign negative weight to an unclicked link that appears above and below a clicked link

7. Marti Hearst, Future of Search ‘07 Actions of the Many vs. the Individual 3.Query auto-suggest based on other users’ queries  vs based on one one’s prior queries alone

8. Marti Hearst, Future of Search ‘07 Social Tagging  Metadata assignment without all the bother  Spontaneous, easy, low cognitive overhead  Usually used in the context of social media

9. Marti Hearst, Future of Search ‘07 Popular pages on del.icio.us

10. Marti Hearst, Future of Search ‘07 Visitor tagging at Powerhouse Museum

11. Tagging is Controversial!  Sloppy!  Disorganized!  Incorrect!  Power to the people!  Easy!  Cheap!

12. Marti Hearst, Future of Search ‘07 Investigating social tagging and folksonomy in the art museumwith steve.museum", J. Trant, B. Wyman, WWW 2006 Collaborative Tagging Workshop Professional Cataloguer: “Everything I know isn't in the picture!”

13. Marti Hearst, Future of Search ‘07 The Tagging Opportunity  At last! Content-oriented metadata in the large!  Attempts at metadata standardization always end up with something like the Dublin Core  author, date, publisher,....  I think the action is in the subject metadata, and have focused on how to navigate collections given such data.

14. Marti Hearst, Future of Search ‘07 The Tagging Opportunity  Tags are inherently faceted !  Multiple labels are assigned to each item  Rather than placing them into a folder  Rather than placing them into a hierarchy  Concepts are assigned from many different content categories

15. Marti Hearst, Future of Search ‘07 Tagging Problems  The haphazard assignments lead to problems with  Synonymy  Homonymy  Unpredictability See how this author attempts to compensate:

16. Marti Hearst, Future of Search ‘07 Tagging Problems  Some tags are fleeting in meaning or too personal  toread todo  Tags don’t “cover” all the concepts  Tags are disorganized  Tags are not “professional”  (I personally don’t think this matters)

17. Marti Hearst, Future of Search ‘07 Research Questions for Tags & Search  How to improve tag convergence?  How to group tags meaningfully? How to eliminate uninteresting tags?  What is the role of user interface on tag convergence?  Preliminary evidence suggests there is a big effect  There are some good ideas out there  More experimentation is needed.  What algorithms can we use to clean up the tags after they are assigned?  There is some work here, much more can be done.  TagAssist: Automatic Tag Suggestion for Blog Posts, Sood et al., ICWSM 2007

18. Marti Hearst, Future of Search ‘07 Interface for adding tags on del.icio.us

19. Marti Hearst, Future of Search ‘07 Effects of Interface On the Structure, Properties and Utility of Internal Corporate Blogs,Kolari et al. ICWSM 2007

20. Marti Hearst, Future of Search ‘07 Research Questions for Tags & Search How to get tag expertise? office desk plants windows shadows Who will identify the plant species in this image?

21. Marti Hearst, Future of Search ‘07 Research Questions for Tags & Search  What is the relationship of social tags to automated content extraction?  Are tags more informative, or differently informative, than other labeling methods?

22. Marti Hearst, Future of Search ‘07 Research Questions for Tags & Society  What motivates people to tag?  Who owns the tags?  Privacy and sharing of tags?

23. Marti Hearst, Future of Search ‘07 Research Questions for Tags & Search  How to use tags for browsing / navigation?  Currently most tags are used as a direct index into items  Click on tag, see items assigned to it, end of story  Grouping into small hierarchies is not usually done  del.icio.us now has bundles, but navigation isn’t good  IBM’s dogear and RawSugar come the closest  One solution: organize tags into faceted hierarchies, use faceted navigation.

24. Faceted Metadata & Navigation

25. Marti Hearst, Future of Search ‘07 The Idea of Faceted Metadata  Create INDEPENDENT categories (facets)  Each facet has labels (sometimes arranged in a hierarchy)  Assign labels from the facets to every item  Example: recipe collection Course Main Course Cooking Method Stir-fry Cuisine Thai Ingredient Bell Pepper Curry Chicken

26. Marti Hearst, Future of Search ‘07 Faceted Navigation  A flexible, dynamic way to allow everyday users browse & search large information collections.  We’ve been investigating and promoting this at UCB since 1999.  It’s now widely used for e-commerce sites;  Digital libraries, image collections, etc., are following.  Search verticals as well  Google co-op  More info: flamenco.berkeley.edu Next Generation Web Search: Setting Our Sites, M. Hearst, IEEE Data Engineering Bulletin,IEEE Data Engineering Bulletin Special issue on Next Generation Web Search, Sept. 2000

27. Marti Hearst, Future of Search ‘07 Faceted Nav in eBay Express

28. Marti Hearst, Future of Search ‘07 Faceted Nav in Digital Libraries  NCSU has a start at it

29. Marti Hearst, Future of Search ‘07 Faceted Nav in eBay Express

30. Marti Hearst, Future of Search ‘07

31. Facets in Google Co-op

32. Marti Hearst, Future of Search ‘07 Advantages of Faceted Navigation  Gives users control and flexibility  Can’t end up with empty results sets  (except with keyword search)  Helps avoid feelings of being lost.  Easier to explore the collection.  Helps users infer what kinds of things are in the collection.  Evokes a feeling of “browsing the shelves”  Is preferred over standard search for collection browsing in usability studies.  (Interface must be designed properly)

33. Marti Hearst, Future of Search ‘07 Advantages of Faceted Metadata  Helps alleviate the metadata wars:  Allows for both splitters and lumpers  Is this a bird or a robin  Doesn’t matter, you can do both!  Allows for differing organizational views  Does NASCAR go under sports or entertainment?  Doesn’t matter, you can do both!

34. How to Create Facet Hierarchies? Our Approach: Castanet (Stoica, Hearst, & Merichar, HLT-NAACL ’07)

35. Marti Hearst, Future of Search ‘07 Example: Recipes (3500 docs)

36. Marti Hearst, Future of Search ‘07 Castanet Output (shown in Flamenco)

37. Marti Hearst, Future of Search ‘07 Castanet Output (shown in Flamenco)

38. Marti Hearst, Future of Search ‘07 Castanet Output (shown in Flamenco)

39. Marti Hearst, Future of Search ‘07 Example: Biology Journal Titles Castanet Output (shown in Flamenco)

40. Marti Hearst, Future of Search ‘07 Castanet Algorithm  Leverage the structure of WordNet Documents WordNet Get hypernym paths Select terms Build tree Compress tree Divide into facets

41. Marti Hearst, Future of Search ‘07 Will Castanet Work on Tags?  Class project by Simon King and Jeff Towle, 2004  1650 captions captured from mobile phones  Wanted to organize them.  Used the CastaNet algorithm  Had to first remove proper names

42. Marti Hearst, Future of Search ‘07 Example Photos & Captions (King & Towle) very scary x-mas treeHp presentation chasing a cat in the dark My cat

43. Marti Hearst, Future of Search ‘07  instrumentality, (112)(112)  vehicle (26)(26)  car (9)(9)  bike (8)(8)  vessel, watercraft (4)(4)  mayflower (2)(2)  ferry (1)(1)  gig (1)(1)  truck (3)(3)  airplane (2)(2)  device (20)(20)  machine (7)(7)  computer (4)(4)  laptop (1)(1)  sander (1)(1)  container (16)(16)  vessel (7)(7)  bottle (5)(5)  water_bottle (2)(2)  jug (1)(1)  pill_bottle (1)(1)  bath (2)(2)  bowl (1)(1)  can (2)(2)  backpack (1)(1)  bumper (1)(1)  empty (1)(1)  salt_shaker (1)(1)  furniture, piece of furniture, article of furniture (12)(12)  seat (8)(8)  bench (2)(2)  chair (2)(2)  couch (2)(2)  lounge (1)(1)  bed (4)(4)  desk (1)(1)

44. Marti Hearst, Future of Search ‘07 Conclusions  The actions of the many are a boon for improving search algorithms.  Social tagging is, in my view, a terrific way to get good content metadata.  I think automated techniques can do a lot to help clean them up and organize them.  They are an inherently social phenomenon, part of social media, which is a really exciting area.

45. Marti Hearst, Future of Search ‘07 Related Work: Automated Tag Organization  Some efforts are on tag prediction:  Mishne ’06:  Uses IR techniques to find the closest tagged documents, uses their tags to assign new tags. Measures on how well new tags predicted  Xu et al. ’06:  Use tags that have already been predicted for a document to predict which to show to a new user who is tagging the document  Some efforts on tag organization:  Brooks & Montanez ’06:  Tries to see if tags can predict document clusters, which in my book aren’t really categories  After clustering based on text they try to induce a tag hierarchy by agglomerative clustering the text. Results not described in detail  Begelman et al. ’06:  Use clustering and tag co-occurrence to find associated tags. Not clear what the organizational goal is

46. Thank you! For more information: flamenco.berkeley.edu