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The Evolution of “Social” Search
from contextualizing queries … to socializing them

Nitya Narasimhan (@nitya)
NYC Semantic Web Meet-up
Mar 25, 2010




        The Anatomy of a Large-Scale                                   The Anatomy of a Large-Scale
       Hyper-textual Web Search Engine                                     Social Search Engine
              S. Brin and L. Page                                      D. Horowitz and S. D. Kamvar
                      1998                                                         2010

                                   Tag Clouds Generated by Wordle
                                        http://www.wordle.net
       Disclaimer: All opinions expressed here are my own and not reflective of my employer or third party
Social + Search – A broad perspective


          Ask a                                                         Contextualize
         machine                       Ranks the Content                (Individual Trust)
                                       (by-value, by-association)




                     Query Interface
     Query                             Social             Social           Response
                                       Media              Graph



             Ask a                                                           Socialize
                                          Ranks the User
             human                     (by-expertise, by-association)
                                                                         (Collective Wisdom)
Social + Search – A broad perspective


          Ask a                                                          Contextualize
         machine                            Ranks the Content            (Individual Trust)
                                       Social-As-Filter
                                            (by-value, by-association)




                     Query Interface
     Query    Social-as-Sensor = “Real Time Search” Response
                             Social     Social
                             Media      Graph



             Ask a                                                            Socialize
                                                  Ranks the User
             human                 Social-As-Router
                                        (by-expertise, by-association)
                                                                          (Collective Wisdom)
Social + Search – Interdependent utility


                                                      Social graph as “trust” metric
                           Social-As-Filter
                                                      Focus on information retrieval
 Twitter
                                                      e.g., Google Social Search (beta)
Face-book
                         Persistence      Curate
 Buzz                      (static)     (authority)
             Real-time
               data
                                                      Crowd as aggregated “pulse”
Foursquare                  Social-as-Sensor
                                                      Focus on information just-in-time
Gowalla
                                                      e.g., OneRiot, Twitter
                          Presence       Persuade
                         (ephemeral)    (influence)
 Amazon

 Reader
                                                      Crowd as “collective” intelligence
                           Social-As-Router           Focus on information expertise
                                                      e.g., Yahoo! Answers, Vark
Evolution of the ‘Contextualized’ Query

             Social-as-Filter (trust)
            Social-as-Sensor (time)

       Exemplifies ‘passive’ social search
          (works with existing data)
Google Social Search (beta) – Social As Filter




              Provides social context to web search results
                                                              http://www.google.com
Increased awareness to social graph and information




                                              Requires user
                                              disclosure of
                                              social graphs

                                               Implicit user
                                              acceptance of
                                               data sharing

                                             Potential for data
                                             exposure out-of-
                                                 context
Google ‘Real-Time’ Search – Social As Sensor




         Provides temporal context to web search results
Google Buzz – Explicit (vs. contextual) search of data




        http://blog.mygazines.com/2010/02/10/google-buzz-how-digital-publishers-can-use-this-social-media-tool/google-buzz-screen/



             Provides direct search of social data (no web)
Challenges for Contextualized Social Search



• Federated User Identity – who owns it?

• Data Privacy
   – Exposing data out-of-context (from original user intent)
   – New assertions by correlating across networks (worlds collide)


• User effort
   – User still in the loop for manual curation of results
   – Weak vs. Strong ties (Twitter Friends vs. Phone Addressbook)
“Socializing” the Query

             Social-as-router

     Exemplifies ‘active’ social search
(generates new data in response to query )
Evolution of a Socialized Query – it’s all in the interface


                                                                       Q&A
                                      Distribution                    Engine
                                                                      Query finds
                                                                       the user
                                          Cha-Cha
                                           KGB
                                           Vark

      Q&A
                                                     Patterns are
     Forums             Destination              not mutually exclusive
     User finds
     the query
                  Yahoo! Answers         TV Answers
                   Baidu Knows        LinkedIn Answers
                  Mahalo Answers
                                         Twelp-Force
                      Hunch

                                                                   Specialized
                                       Domain                         Q&A
                                                                    Focused interests
                                                                   (defined expertise,
                                                                   built-in incentives)
Yahoo Answers: The established Q&A Destination




                                            http://answers.yahoo.com
Mahalo Answers: New ways to incentivize participants




                                             http://www.mahalo.com
Hunch – From recommendations to ‘taste-graphs’




                                            http://www.hunch.com
ChaCha/KGB – Distributing the query. Paid Experts.




                 Decentralized interfaces (IM, SMS, Email, Twitter)
http://kgb.com                                               http://chacha.com
ChaCha.me – evolving to collective intelligence




                                                  http://chacha.me
Vark – The “Social Search” Engine (acquired by Google)




      True crowd-sourcing of query (with intelligent selection)
                                                       http://www.vark.com
Challenges in Socialized Queries



• Query Formulation – “How” to frame the question?
• Query Routing – “Whom” to ask?
• Query Visibility – How to prevent query starvation?

• Response Gathering – “How many” before we’re confident
• Response Grading – How do we rate the response?
• Response Delivery – “How” and “where” to send replies

• Participation incentives – benefits vs. costs
The Domain Difference



• Niche Q&A forums or interfaces that bring in domain
  context or semantic knowledge to improve utility
   – LinkedIn Answers: Q&A for professional/career topics
   – TwelpForce: Best Buy Q&A Twitter “bot” for directed queries
   – TV Answers: Q&A around visual (rich media) queries


• The “TV Answers” System (an domain-specific example)
   – The Vocabulary problem – asking questions related to rich visual
     context on a search engine is hard.
   – The Recognition problem – humans are faster and more adept at
     recognizing visual cues (compared to machines)
Recommended Reading ..



•   Search in Social Media Workshops (SSM)
•   Community Question-Answering (Eugene Agichtein)
•   Knowledge-Sharing Networks (Lada Adamic)
•   Social Strategies for Search (Brynn Evans)

•   The Inner Workings of a Real-time Search Engine (OneRiot)
•   Anatomy of a Large-Scale Social Search Engine (Vark)
•   Introducing Google Search (Google Search Beta)
•   How Hunch Works (Hunch)
The TV Answers System
       Social Search for Rich Media Queries
          N. Narasimhan, J. Wodka, V. Vasudevan



“TV Answers: using the wisdom of crowds to facilitate searches with rich
                      media context” (CCNC 2010)
  Paper Available at: http://techpubs.motorola.com/IPCOM/189001




            Motorola Applied Research Center
TV Answers – Bring ‘explicit’ social search to TV

• Focus on user-generated queries around viewed content
• Real-world examples (from Yahoo! Answers ‘TV’ category)




• Goal – instrument user-facing interfaces to leverage semantic
  and behavioral knowledge specific to the domain
What’s the challenge in ‘inline’ TV Search?

                                  How to ask the question?

                                 ‘Search’ interfaces on TV are
                                cumbersome, limited in facets

                                Human questions tend to be
                                  ambiguous, imprecise


                               Whom to target for responses?

                                  Humans excel (over SE) in
                                visual interpretation, intuitive
                               query ‘tuning’, collective wisdom
The “TV Answers” System

                           How to ask the question?

                           ‘Freeze-Frame’ interface to
                              capture visual context

                           ‘Templates’ helper to ease
                                query creation



                                Whom to ask?

                          ‘Edge proxy’ intermediary to
                          route query to relevant user
                           communities for responses
Simplified Architecture and Interaction Flow

                      Client (STB)                             Proxy (Edge)                             Community (Web)




                         Query




                                                                                   Community Adapters
                                                           Query        Query
   User                 Creation
                                         TV Answers API
                                                          Expansion    Targeting




                      Response                            Response     Response
                       Delivery                            Grading     Gathering
Response
Review or
Alert                                                                                                    Search
                      Query Editing                                                                      Engine


                                 “Proprietary”                                         “Open”
    Client (Mobile)                                                                    Internet
                                   Network
V1 Prototype – OCAP Client Screenshots
   Query Generation




                           Freeze & Focus – capture query context     Annotate & Submit – provide query text
    Response Gathering




                         Query Dashboard – view submitted queries   Response Dialog – view response details
Questions?

    For more information:
    nitya@motorola.com
http://www.twitter.com/nitya
Twitter @Anywhere (decentralized integration)
Mechanical Turk: crowd-source the task (not the query)
Social Retail Domain: The Best Buy Case Study

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The Evolution of Social Search

  • 1. The Evolution of “Social” Search from contextualizing queries … to socializing them Nitya Narasimhan (@nitya) NYC Semantic Web Meet-up Mar 25, 2010 The Anatomy of a Large-Scale The Anatomy of a Large-Scale Hyper-textual Web Search Engine Social Search Engine S. Brin and L. Page D. Horowitz and S. D. Kamvar 1998 2010 Tag Clouds Generated by Wordle http://www.wordle.net Disclaimer: All opinions expressed here are my own and not reflective of my employer or third party
  • 2. Social + Search – A broad perspective Ask a Contextualize machine Ranks the Content (Individual Trust) (by-value, by-association) Query Interface Query Social Social Response Media Graph Ask a Socialize Ranks the User human (by-expertise, by-association) (Collective Wisdom)
  • 3. Social + Search – A broad perspective Ask a Contextualize machine Ranks the Content (Individual Trust) Social-As-Filter (by-value, by-association) Query Interface Query Social-as-Sensor = “Real Time Search” Response Social Social Media Graph Ask a Socialize Ranks the User human Social-As-Router (by-expertise, by-association) (Collective Wisdom)
  • 4. Social + Search – Interdependent utility Social graph as “trust” metric Social-As-Filter Focus on information retrieval Twitter e.g., Google Social Search (beta) Face-book Persistence Curate Buzz (static) (authority) Real-time data Crowd as aggregated “pulse” Foursquare Social-as-Sensor Focus on information just-in-time Gowalla e.g., OneRiot, Twitter Presence Persuade (ephemeral) (influence) Amazon Reader Crowd as “collective” intelligence Social-As-Router Focus on information expertise e.g., Yahoo! Answers, Vark
  • 5. Evolution of the ‘Contextualized’ Query Social-as-Filter (trust) Social-as-Sensor (time) Exemplifies ‘passive’ social search (works with existing data)
  • 6. Google Social Search (beta) – Social As Filter Provides social context to web search results http://www.google.com
  • 7. Increased awareness to social graph and information Requires user disclosure of social graphs Implicit user acceptance of data sharing Potential for data exposure out-of- context
  • 8. Google ‘Real-Time’ Search – Social As Sensor Provides temporal context to web search results
  • 9. Google Buzz – Explicit (vs. contextual) search of data http://blog.mygazines.com/2010/02/10/google-buzz-how-digital-publishers-can-use-this-social-media-tool/google-buzz-screen/ Provides direct search of social data (no web)
  • 10. Challenges for Contextualized Social Search • Federated User Identity – who owns it? • Data Privacy – Exposing data out-of-context (from original user intent) – New assertions by correlating across networks (worlds collide) • User effort – User still in the loop for manual curation of results – Weak vs. Strong ties (Twitter Friends vs. Phone Addressbook)
  • 11. “Socializing” the Query Social-as-router Exemplifies ‘active’ social search (generates new data in response to query )
  • 12. Evolution of a Socialized Query – it’s all in the interface Q&A Distribution Engine Query finds the user Cha-Cha KGB Vark Q&A Patterns are Forums Destination not mutually exclusive User finds the query Yahoo! Answers TV Answers Baidu Knows LinkedIn Answers Mahalo Answers Twelp-Force Hunch Specialized Domain Q&A Focused interests (defined expertise, built-in incentives)
  • 13. Yahoo Answers: The established Q&A Destination http://answers.yahoo.com
  • 14. Mahalo Answers: New ways to incentivize participants http://www.mahalo.com
  • 15. Hunch – From recommendations to ‘taste-graphs’ http://www.hunch.com
  • 16. ChaCha/KGB – Distributing the query. Paid Experts. Decentralized interfaces (IM, SMS, Email, Twitter) http://kgb.com http://chacha.com
  • 17. ChaCha.me – evolving to collective intelligence http://chacha.me
  • 18. Vark – The “Social Search” Engine (acquired by Google) True crowd-sourcing of query (with intelligent selection) http://www.vark.com
  • 19. Challenges in Socialized Queries • Query Formulation – “How” to frame the question? • Query Routing – “Whom” to ask? • Query Visibility – How to prevent query starvation? • Response Gathering – “How many” before we’re confident • Response Grading – How do we rate the response? • Response Delivery – “How” and “where” to send replies • Participation incentives – benefits vs. costs
  • 20. The Domain Difference • Niche Q&A forums or interfaces that bring in domain context or semantic knowledge to improve utility – LinkedIn Answers: Q&A for professional/career topics – TwelpForce: Best Buy Q&A Twitter “bot” for directed queries – TV Answers: Q&A around visual (rich media) queries • The “TV Answers” System (an domain-specific example) – The Vocabulary problem – asking questions related to rich visual context on a search engine is hard. – The Recognition problem – humans are faster and more adept at recognizing visual cues (compared to machines)
  • 21. Recommended Reading .. • Search in Social Media Workshops (SSM) • Community Question-Answering (Eugene Agichtein) • Knowledge-Sharing Networks (Lada Adamic) • Social Strategies for Search (Brynn Evans) • The Inner Workings of a Real-time Search Engine (OneRiot) • Anatomy of a Large-Scale Social Search Engine (Vark) • Introducing Google Search (Google Search Beta) • How Hunch Works (Hunch)
  • 22. The TV Answers System Social Search for Rich Media Queries N. Narasimhan, J. Wodka, V. Vasudevan “TV Answers: using the wisdom of crowds to facilitate searches with rich media context” (CCNC 2010) Paper Available at: http://techpubs.motorola.com/IPCOM/189001 Motorola Applied Research Center
  • 23. TV Answers – Bring ‘explicit’ social search to TV • Focus on user-generated queries around viewed content • Real-world examples (from Yahoo! Answers ‘TV’ category) • Goal – instrument user-facing interfaces to leverage semantic and behavioral knowledge specific to the domain
  • 24. What’s the challenge in ‘inline’ TV Search? How to ask the question? ‘Search’ interfaces on TV are cumbersome, limited in facets Human questions tend to be ambiguous, imprecise Whom to target for responses? Humans excel (over SE) in visual interpretation, intuitive query ‘tuning’, collective wisdom
  • 25. The “TV Answers” System How to ask the question? ‘Freeze-Frame’ interface to capture visual context ‘Templates’ helper to ease query creation Whom to ask? ‘Edge proxy’ intermediary to route query to relevant user communities for responses
  • 26. Simplified Architecture and Interaction Flow Client (STB) Proxy (Edge) Community (Web) Query Community Adapters Query Query User Creation TV Answers API Expansion Targeting Response Response Response Delivery Grading Gathering Response Review or Alert Search Query Editing Engine “Proprietary” “Open” Client (Mobile) Internet Network
  • 27. V1 Prototype – OCAP Client Screenshots Query Generation Freeze & Focus – capture query context Annotate & Submit – provide query text Response Gathering Query Dashboard – view submitted queries Response Dialog – view response details
  • 28. Questions? For more information: nitya@motorola.com http://www.twitter.com/nitya
  • 30. Mechanical Turk: crowd-source the task (not the query)
  • 31. Social Retail Domain: The Best Buy Case Study