Social Tagging and Search Marti Hearst UC Berkeley

2 Marti Hearst, iConfernece ‘06 Search Topical Metadata Structured, Flexible Navigation

3 Marti Hearst, iConfernece ‘06 The Idea of Facets  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

4 Marti Hearst, iConfernece ‘06 Using Facets  Allow multiple ways to get to each item Preparation Method Fry Saute Boil Bake Broil Freeze Desserts Cakes Cookies Dairy Ice Cream Sherbet Flan Fruits Cherries Berries Blueberries Strawberries Bananas Pineapple Fruit > Pineapple Dessert > Cake Preparation > Bake Dessert > Dairy > Sherbet Fruit > Berries > Strawberries Preparation > Freeze

5 Marti Hearst, iConfernece ‘06 Opening View Select literature from PRIZE facet

6 Marti Hearst, iConfernece ‘06 Group results by YEAR facet

7 Marti Hearst, iConfernece ‘06 Select 1920’s from YEAR facet

8 Marti Hearst, iConfernece ‘06 Current query is PRIZE > literature AND YEAR: 1920’s. Now remove PRIZE > literature

9 Marti Hearst, iConfernece ‘06 Now Group By YEAR > 1920’s

10 Marti Hearst, iConfernece ‘06 Advantages of the Approach  Systematically integrates search results:  reflect the structure of the info architecture  retain the context of previous interactions  Gives users control and flexibility  Over order of metadata use  Over when to navigate vs. when to search  Allows integration with advanced methods  Collaborative filtering, predicting users’ preferences

11 Marti Hearst, iConfernece ‘06 Faceted Digital Libraries  NCSU has a start at it

Problem with Metadata-Oriented Approaches Getting the metadata!

13 Marti Hearst, iConfernece ‘06 Search Topical Metadata Social question answering Recorded Human Interaction Click-through ranking Inferred recommendations

14 Marti Hearst, iConfernece ‘06 Human Real-time Question Answering  More popular in Korea than algorithmic search  Maybe fewer good web pages?  Maybe more social society?  Several examples in US:  Yahoo answers recently released and successful  wondir.com  answerbag.com

15 Marti Hearst, iConfernece ‘06 Yahoo Answers (also answerbag.com, wondir.com, etc)

16 Marti Hearst, iConfernece ‘06 Yahoo Answers appearing in search results

17 Marti Hearst, iConfernece ‘06 answerbag.com

18 Marti Hearst, iConfernece ‘06 Using User Behavior as Implicit Preferences  Search click-through experimentally shown to boost search rankings for top results  Joachims et al. ‘05, Agichtein et al. ‘06  Works ok even if non-relevant documents examined  Best in combination with sophisticated search algorithms  Doesn’t work well for ambiguous queries  Aggregates of movie and book selections comprise implicit recommendations

19 Marti Hearst, iConfernece ‘06 Search Topical Metadata Recorded Human Interaction Social Tagging (photos, bookmarks) Game-based tagging

20 Marti Hearst, iConfernece ‘06 Social Tagging  Metadata assignment without all the bother  Spontaneous, easy, and tends towards single terms

21 Marti Hearst, iConfernece ‘06 Issues with Photo and Web link Tagging  There is a strong personal component  Marking for my own reminders  Marking for my circle of friends  There is also a strong social component  Try to promote certain tags to make them more popular, or post to popular tags to see your influence rise

22 Marti Hearst, iConfernece ‘06 Tagging Games  Assigning metadata is fun! (ESP game, von Ahn)  No need for reputation system, etc.  Pay people to do it  MyCroft (iSchool student project)  Drawback: least common denominator labels  Experts already label their own data or that about which they have expertise  E.g., protein function  Wikipedia

23 Marti Hearst, iConfernece ‘06 Search Topical Metadata Social question answering Recorded Human Interaction Social Tagging (photos, bookmarks) Click-through ranking Inferred recommendations Game-based tagging ????

24 Marti Hearst, iConfernece ‘06 Expert-Oriented Tagging in Search  Already happening at Google co-op  Shows up in certain types of search results

25 Marti Hearst, iConfernece ‘06 Expert-Oriented Tagging  Already happening at Google co-op  Shows up in certain types of search results

26 Marti Hearst, iConfernece ‘06 Promoting Expertise-Oriented Tagging  Research area: User Interfaces  To make rapid-feedback suggestions of pre- established tags  Like type-ahead queries  To incentivize labeling and make it fun  To allow the personal aspects to shine through

27 Marti Hearst, iConfernece ‘06 Promoting Expertise-Oriented Tagging  Research area: NLP Algorithms  (We have an algorithm to build facets from text)  To convert tags into facet hierarchies  To capture implicit labeling information

28 Marti Hearst, iConfernece ‘06 Promoting Expertise-Oriented Tagging  Research area: Digital infrastructure  Extending tagging games  Build an architecture that channels specialized subproblems to appropriate experts  We now know there is a green plant in an office; direct this to the botany > houseplants experts

29 Marti Hearst, iConfernece ‘06 Promoting Expertise-Oriented Tagging  Research area: economics and sociology  What are the right incentive structures?

30 Marti Hearst, iConfernece ‘06 Using Implicit Preferences  Extend implicit recommendation technology to online catalog use

31 Marti Hearst, iConfernece ‘06 Summary  There is great potential in tapping the social information use channel  To improve metadata  To improve integration with search  The necessary research is interdisciplinary!