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Social Tagging and Search

Social Tagging and Search. Marti Hearst UC Berkeley. Search. Topical Metadata. Structured, Flexible Navigation. 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

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Social Tagging and Search

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  1. Social Tagging and Search Marti Hearst UC Berkeley

  2. Search Topical Metadata Structured, Flexible Navigation

  3. 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 Ingredient Cooking Method Chicken Stir-fry Bell Pepper Curry Course Cuisine Main Course Thai

  4. 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. Opening ViewSelect literature from PRIZE facet

  6. Group results by YEAR facet

  7. Select 1920’s from YEAR facet

  8. Current query is PRIZE > literature ANDYEAR: 1920’s. Now remove PRIZE > literature

  9. Now Group By YEAR > 1920’s

  10. 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. Faceted Digital Libraries • NCSU has a start at it

  12. Getting the metadata! Problem with Metadata-Oriented Approaches

  13. Search Topical Metadata Recorded Human Interaction Social question answering Click-through ranking Inferred recommendations

  14. 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. Yahoo Answers (also answerbag.com, wondir.com, etc)

  16. Yahoo Answers appearing in search results

  17. 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

  18. Search Topical Metadata Recorded Human Interaction Social Tagging (photos, bookmarks) Game-based tagging

  19. Social Tagging • Metadata assignment without all the bother • Spontaneous, easy, and tends towards single terms

  20. 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

  21. 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

  22. Search Topical Metadata Recorded Human Interaction Social question answering Social Tagging (photos, bookmarks) Click-through ranking Game-based tagging Inferred recommendations ????

  23. Expert-Oriented Tagging in Search • Already happening at Google co-op • Shows up in certain types of search results

  24. Expert-Oriented Tagging • Already happening at Google co-op • Shows up in certain types of search results

  25. 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

  26. 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

  27. 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

  28. Promoting Expertise-Oriented Tagging • Research area: economics and sociology • What are the right incentive structures?

  29. Using Implicit Preferences • Extend implicit recommendation technology to online catalog use

  30. 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!

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