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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 ...

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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The Evolution of Social Search The Evolution of Social Search Presentation Transcript

  • 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