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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 l.jpg

Social Tagging and Search

Marti Hearst

UC Berkeley


Slide2 l.jpg

Search

Topical

Metadata

Structured, Flexible

Navigation


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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


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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


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Opening ViewSelect literature from PRIZE facet




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



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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


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Faceted Digital Libraries

  • NCSU has a start at it


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Getting the metadata!

Problem with Metadata-Oriented Approaches


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Search

Topical

Metadata

Recorded Human Interaction

Social question

answering

Click-through

ranking

Inferred

recommendations


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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


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



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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


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Search

Topical

Metadata

Recorded Human Interaction

Social Tagging

(photos, bookmarks)

Game-based

tagging


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Social Tagging

  • Metadata assignment without all the bother

  • Spontaneous, easy, and tends towards single terms


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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


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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


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Search

Topical

Metadata

Recorded Human Interaction

Social question

answering

Social Tagging

(photos, bookmarks)

Click-through

ranking

Game-based

tagging

Inferred

recommendations

????


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Expert-Oriented Tagging in Search

  • Already happening at Google co-op

  • Shows up in certain types of search results


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Expert-Oriented Tagging

  • Already happening at Google co-op

  • Shows up in certain types of search results


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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


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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


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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


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Promoting Expertise-Oriented Tagging

  • Research area: economics and sociology

    • What are the right incentive structures?


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Using Implicit Preferences

  • Extend implicit recommendation technology to online catalog use


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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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