Future of Search | Yury Lifshits, Yahoo! Research

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    Future of Search | Yury Lifshits, Yahoo! Research - Presentation Transcript

    1. Yury Lifshits Yahoo! Research http://yury.name Future of Search St. Petersburg | Helsinki December 2008
    2. Outline
      • Structured Search
      • Yahoo! Work in Search
        • SearchMonkey
        • BOSS
      • Research Agenda
    3. Structured Search: work in progress
    4. Structured Search = Bring structured data to search users M.K. Bergman. The Deep Web: Surfacing Hidden Value. 2001.
    5. Value Proposition
      • Coverage
        • Real-time data
        • Semi-private data
      • Structured queries
      • Ordering and filtering results
      • Straight-to-answers
    6. User Interface: Query
      • Search assist: Yahoo!
      • Selector: LinkedIn, VKontakte.ru
      • Multiple search buttons: Gmail
      • Search tabs: Yahoo / Google
    7. User Interface: Results
      • Federated page
      • Facets
      • Search transfer / search form
      K.P. Yee, K. Swearingen, K. Li, M. Hearst. Faceted metadata for image search and browsing. CHI 2003. Fernando Diaz. Aggregation of News Content Into Web Results. WSDM 2009. http:// glue.yahoo.com http:// au.alpha.yahoo.com
    8.  
    9.  
    10. Data Supply Chain
      • Atomic fact
      • Flight, Event, Patent
      • Data aggregator
      • US Patents, Amadeus/Sabre flights, Upcoming.com
      • Domain search
      • Expedia, Spock
      • General purpose search
      • Yahoo!, Google, Yandex, Baidu
    11. Getting structured data
      • Entity extraction
      • Markup
      • Feeds
      • Search API (OpenSearch)
      • OR
      • Do a search transfer
    12. Give Us Your Data For …
      • Traffic via search transfer
      • Firefox search box
      • Better presentation in search
      • SearchMonkey
      • Hosted search
      • BOSS Custom
      • Showing your ads
      • Yahoo Local + AT&T
    13. Yahoo! Work in Search
    14. Slides by: Paul Tarjan, Chief Technical Monkey ( [email_address] ) Full version http://www.slideshare.net/ptarjan/searchmonkey-presentation
    15. What is SearchMonkey? an open platform for using structured data to build more useful and relevant search results Before After
    16. Enhanced Result: Zagat Key/Value Pairs or Abstract Links Image
    17. Infobar: Wikipedia Preview Summary Blob
    18. Creating an Infobar
      • Infobar advantages
        • Annotate someone else’s site
        • Use links and images from other domains
          • Mash up info from multiple sites
          • Affiliate / coupon links? Hmmm…
        • Can act on *, all websites
          • But these apps can be annoying if poorly designed
      • Key design principles
        • Put something useful in the summary
        • Be creative with the HTML
    19. How to get data to SearchMonkey?
      • Humans see:
      • name
      • picture of a person
      • current job
      • industry, …
      • Computers see:
      • an undifferentiated
      • blob of HTML
      • Can we make computers smarter?
    20. How does it work? Acme.com’s database Index RDF/Microformat Markup site owners/publishers share structured data with Yahoo!. 1 consumers customize their search experience with Enhanced Results or Infobars 3 site owners & third-party developers build SearchMonkey apps. 2 DataRSS feed Web Services Page Extraction Acme.com’s Web Pages
    21. SearchMonkey Resources
      • Main:
        • http:// developer.yahoo.com/searchmonkey
      • Lists and forums:
        • [email_address]
        • http:// suggestions.yahoo.com/searchmonkey
    22. Vik Singh (Architect) Graham Mudd (Senior PMM)
    23. BOSS = B uild your O wn S earch S ervice Open Yahoo’s core search features via web services to let 3rd parties revolutionize Search Unrestricted What
      • Unrestricted:
      • Unlimited queries
      • Blend, re-order, discard
      • Full presentation control
      • Non-search apps OK
      • Monetization: Free or CPM or Ads
      What
      • Barriers to entry are massive
      • $300M, top talent, a prayer to get to basic parity
      • No monopoly over great ideas
      • Search anywhere
      • Improve Vertical Quality w/ Web comprehensiveness
      • Fragment the market, foster more players, choice, competition
      • Yahoo extends advertising reach, 3rd parties revenue share
      Why
    24. Why Traditional Search Distribution + BOSS Distribution
    25. Tracks API A self-service, web services model for developers and start-ups to quickly build and deploy new search experiences.
        • UIUC
        • CMU
        • Stanford
        • Purdue
        • IIT Bombay
        • MIT
        • UMass
      CUSTOM Working with 3rd parties to build a more relevant, brand/site specific web search experience. This option is jointly built by Yahoo! and select partners.
        • ACADEMIC
        • Working with the following universities to allow for wide-scale research in the search field:
      Interested in Custom? Email us [email_address]
    26. http://boss.yahooapis.com/ysearch/{vert}/v1/{q} {vert} := {web, news, images, spelling} @ required appid @ optional (Y!OS compliant) start, count, lang, region, format, callback, sites BOSS API v1
    27. Python (v2.5+) library BOSS Search SDK plus … SQL for remixing arbitrary XML/JSON sources Loosely Functional programming paradigm BOSS Mashup Framework
    28. Ported enhanced version of BMF to GAE platform http://zooie.wordpress.com/2008/08/04/yahoo-boss-google-app-engine-integrated/ Easiest way to deploy a BOSS application online BMF + Google App Engine
    29. http://www.4hoursearch.com http://123people.com Mashable! Contest for BOSS search engines http://mashable.com/boss/ Examples
    30. BOSS Custom for TechCrunch
    31. TechCrunch Network Search
      • CrunchBase + Posts + Web
      • Sort by time / relevance
      • Enhanced results
      • Domain-specific facets
      • Yahoo! sponsored search
      • Real-time indexing
      • Special results
    32. Research Agenda
    33. Structured Search
      • Analysis of search demand
        • Intent classification
        • General search vs. vertical
      • Incentives in data supply
      • Push & real-time indexing
      • Search user interface
        • One box vs. multi-box
        • General vs. vertical
      • Deciding search transfer
        • When?
        • To whom?
    34. Key Scientific Challenges Draft: http://research.yahoo.com/ksc
      • Search intent
      • Quality metrics
      • Web mining
      • Multilingual IR
      • Nextgen search
        • Synthesized result pages
      • World knowledge
      A.Z. Broder. Taxonomy of web search. SIGIR 2002.
    35. More Problems
      • Discovery search
      • Web search vs. asking people
      • Event search
    36. Thanks for your attention!
      • Yury Lifshits
      • http://yury.name
      • [email_address]

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