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

Nitya Narasimhan (@nitya)
NYC Semanti...
Social + Search – A broad perspective


          Ask a                                                         Contextual...
Social + Search – A broad perspective


          Ask a                                                          Contextua...
Social + Search – Interdependent utility


                                                      Social graph as “trust” m...
Evolution of the ‘Contextualized’ Query

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

      ...
Google Social Search (beta) – Social As Filter




              Provides social context to web search results
           ...
Increased awareness to social graph and information




                                              Requires user
      ...
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-di...
Challenges for Contextualized Social Search



• Federated User Identity – who owns it?

• Data Privacy
   – Exposing data...
“Socializing” the Query

             Social-as-router

     Exemplifies ‘active’ social search
(generates new data in res...
Evolution of a Socialized Query – it’s all in the interface


                                                            ...
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)...
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)...
Challenges in Socialized Queries



• Query Formulation – “How” to frame the question?
• Query Routing – “Whom” to ask?
• ...
The Domain Difference



• Niche Q&A forums or interfaces that bring in domain
  context or semantic knowledge to improve ...
Recommended Reading ..



•   Search in Social Media Workshops (SSM)
•   Community Question-Answering (Eugene Agichtein)
•...
The TV Answers System
       Social Search for Rich Media Queries
          N. Narasimhan, J. Wodka, V. Vasudevan



“TV A...
TV Answers – Bring ‘explicit’ social search to TV

• Focus on user-generated queries around viewed content
• Real-world ex...
What’s the challenge in ‘inline’ TV Search?

                                  How to ask the question?

                 ...
The “TV Answers” System

                           How to ask the question?

                           ‘Freeze-Frame’ in...
Simplified Architecture and Interaction Flow

                      Client (STB)                             Proxy (Edge) ...
V1 Prototype – OCAP Client Screenshots
   Query Generation




                           Freeze & Focus – capture query c...
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

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An overview of the social search space -- tracing its evolution from contextualized query (augmenting machine search with social data) to socialized query (connecting the asker directly to users or to collective knowledge accumulated from social interactions). The presentation gives examples of various sites and goes into some detail on a vertical socialized query example for Television and Rich Media

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Transcript of "The Evolution of Social Search"

  1. 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. 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. 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. 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. 5. Evolution of the ‘Contextualized’ Query Social-as-Filter (trust) Social-as-Sensor (time) Exemplifies ‘passive’ social search (works with existing data)
  6. 6. Google Social Search (beta) – Social As Filter Provides social context to web search results http://www.google.com
  7. 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. 8. Google ‘Real-Time’ Search – Social As Sensor Provides temporal context to web search results
  9. 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. 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. 11. “Socializing” the Query Social-as-router Exemplifies ‘active’ social search (generates new data in response to query )
  12. 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. 13. Yahoo Answers: The established Q&A Destination http://answers.yahoo.com
  14. 14. Mahalo Answers: New ways to incentivize participants http://www.mahalo.com
  15. 15. Hunch – From recommendations to ‘taste-graphs’ http://www.hunch.com
  16. 16. ChaCha/KGB – Distributing the query. Paid Experts. Decentralized interfaces (IM, SMS, Email, Twitter) http://kgb.com http://chacha.com
  17. 17. ChaCha.me – evolving to collective intelligence http://chacha.me
  18. 18. Vark – The “Social Search” Engine (acquired by Google) True crowd-sourcing of query (with intelligent selection) http://www.vark.com
  19. 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. 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. 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. 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. 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. 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. 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. 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. 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. 28. Questions? For more information: nitya@motorola.com http://www.twitter.com/nitya
  29. 29. Twitter @Anywhere (decentralized integration)
  30. 30. Mechanical Turk: crowd-source the task (not the query)
  31. 31. Social Retail Domain: The Best Buy Case Study

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