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Recommendation Systems PowerPoint Presentation
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Recommendation Systems

Recommendation Systems

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Recommendation Systems

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  1. Sheikh BilalYousuf Recommendation Systems

  2. Agenda Focus of my presentation • Practical side • Discuss Examples • Business Implications

  3. Why RS?? • 93% of the information produced worldwide is in Digital Format • 623 Exabytes of Data • Exabyte? • 5 to 8 Exabytes of traffic / Month http://en.wikipedia.org/wiki/Exabyte http://www.c-i-a.com/

  4. Why RS?? - 2 • 1.59 billion Internet Users • 24% of the World’s population • 213 million search queries a day http://www.c-i-a.com/pr0509.htm http://searchenginewatch.com/2156461

  5. Why RS?? - 3 • RS – problem of information filtering • RS – problem of machine learning • Enhance user experience • Assist users in finding information • Reduce search and navigation time • Increase productivity • Increase credibility • Mutually beneficial proposition

  6. Are RS = Search Engines? • Radically Different Concepts • SE • Input -> Query Processing -> Results • RS • Behavior Analysis • Learns users personality • Currently SE>RS • Turnaround possible? http://machine-learning.blogspot.com/2009/12/from-search-to-recommender-systems.html

  7. Paradigm Shift • Google Personalized Search “Beginning today, Google will now personalize the search results of anyone who uses its search engine, regardless of whether they’ve opted-in to a previously existing personalization feature.“ (Dec 4, 2009) • SEOs R.I.P

  8. Paradigm Shift -2 • Other players • Yahoo Search Builder • Eurekster • Rollyo • Ask

  9. Googling “Ubuntu Jaunty Jackalope” (Query by Salman)

  10. Googling “Ubuntu Jaunty Jackalope” (Query by Me)

  11. Googling “Ubuntu Jaunty Jackalope” (Query by Coolguy)

  12. Youtube

  13. RS in e-industries • Movies • Music • Search Engines • Retail • Auction, etc

  14. Netflix • Movie Rental • Choose from 65000 titles • 5 million active customers • Ship 1.4M disks per day from 40 locations • 1.4B ratings since 1997 • 2M ratings per day • 1B predictions per day • Netflix Challenge Stats from 2006, before the Netflix Challenge http://blog.recommenders06.com/wp-content/uploads/2006/09/bennett.pdf

  15. Give Ratings, get Recommendations

  16. Become the principal music service for consumers globally • Replace broadcast radio/TV as the primary source of music listening and discovery • Replace CDs as the primary way to listen to music on-demand http://blog.recommenders06.com/wp-content/uploads/2006/09/beaupre.pdf

  17. #1 Music Site (Unique Users) – July ’06 • Yahoo! Music 25.5 mil. • iTunes Application 20.1 mil. • AOL 17.4 mil. • MTV Music 12.2 mil. • MySpace Music 11.2 mil. • #1 in total usage minutes per month – July ‘06 • Yahoo! Music 600 mil. • AOL 265 mil. • MTV 119 mil. • MySpace Music 48 mil. http://blog.recommenders06.com/wp-content/uploads/2006/09/beaupre.pdf

  18. Y! Music - Personalization • Over 7 billion explicit user music ratings • Over 30 million user-customized LAUNCHcast radio-stations

  19. Business Goals • Increased Levels of: • Engagement • Trust, loyalty, retention • Switching costs • Media and transaction revenue • Leads to • Convenience • Quality • Control • Discovery http://blog.recommenders06.com/wp-content/uploads/2006/09/beaupre.pdf

  20. Strands Inc. • Develops technologies to better understand people’s taste and help them discover things they like and didn’t know about • Social Recommender Engine • provides real-time recommendations of products and services • http://www.crunchbase.com/company/strands

  21. Strands Inc. - 2 • Operate in 3 areas • Business Solutions • Personal Finance (moneyStrands) • Social Discovery

  22. StumbleUpon • A web community • 8,865,182 members • Users can discover and rate Web pages, photos, and videos • It is a personalized recommendation engine which uses peer and social-networking principles. SOCIAL DISCOVERY! • http://www.crunchbase.com/company/strands • http://www.stumbleupon.com/

  23. www.stumbleupon.com

  24. Psychology Interests me…