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Iletken recommendation technologies solution


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iletken recommendation technologies
iletken tavsiye sistemleri tanıtım

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Iletken recommendation technologies solution

  1. 1. Social Recommendation Technologies<br />
  2. 2. Recommending items of interest to users<br />based on explicit or implicit preferneces<br />Problem?<br />It is the browsing that holds the golden opportunity for a recommendation system, because the user is not focused on finding a specific thing – she is open to suggestions. <br />Alex Iskold, ReadWriteWeb 2007<br />
  3. 3. User Frustration…<br />…Lost Business Opportunity<br />
  4. 4. with<br />Increase Usage and Sales between %10-50<br />by<br />connecting<br />the right content<br />the right user<br />* iletken for Mobile Content Recommendations slide<br />
  5. 5. You Need To<br />Understand the User<br />Understand the Content<br />For Giving<br />Right Content<br />to the <br />Right User<br />
  6. 6. Content<br />Social & User Network<br />User action<br />iletken Recommender System<br />Interactions<br />Content and Context<br />Customized Solution<br />Business <br />Client<br />Analytics and Feedback<br />Real Time Recommendations<br />
  7. 7. Benefits<br />Monetize Niche Content<br />The bottom line is…<br />Generate Cross Sales<br />Increase Usability<br />Sales Increase<br />10% - 50%<br />Better Customer Service<br />Targeted Reach<br />… and more<br />
  8. 8. Awards and Global Recognition<br />3rd best recommender startup at ACM’s RecSys’08…<br />… out of 26 projects from 15 countries worldwide<br />“GeleceğıninternetindeTürkimzası.” <br />CNN Türk ’08<br />“One of 5 early recommendation technologies that could shake up their niches.”<br />ReadWriteWeb ‘08<br />iletken is a proud software partner of intel<br />iletken R&D is supported by TÜBİTAK<br />
  9. 9. Our Hybrid Technology<br />Behavior based<br />Content based<br />Social Relevancybased<br />Context based proximity graphs<br />Natural language processing<br />Collaborative filtering<br />Metadata analysis<br />MachineLearning<br />vs<br />
  10. 10. About iletken Technologies<br />
  11. 11. iletken for Media Content Recommendations<br />
  12. 12. iletken for Mobile Content Recommendations<br />Personalized targeting for…<br />Life – Ukraine results<br />… mobile game downloads and melodies<br />%331 Elevation on Niche Content<br />%411 Elevation on Popular Content<br />Overall %35-50 increase in subscription<br />
  13. 13. iletken for E-Commerce Recommendations<br />
  14. 14. Management Team<br /> Selçuk ATLI - CIO<br /><ul><li>Semantic Web and Recommender Systems LAB, TW
  15. 15. Fulbright Scholar and M.S. Information Technology @ RPI</li></ul>M. Deniz OKTAR - CEO<br /><ul><li>Founded ReklamGiy</li></ul>Barış Can DAYLIK - CTO<br /><ul><li>Natural Language Processing & MachineLearning
  16. 16. Pardus commiter</li></li></ul><li>Thanks<br />Contact<br />Visit<br />Next: More on recommender technology<br />
  17. 17. Next: More on RecommendationTechnologies<br />1. Real WorldExample: Salesman<br />2. RecommendationMethodsDetailed<br />
  18. 18. The Salesman Analogy<br />
  19. 19. A salesmen is a Recommender<br />Recommending the right house <br />for the <br />right family<br />Difficult but why? <br /><ul><li>Needs to know about the item
  20. 20. Needs to know about the buyers
  21. 21. Needs experience </li></li></ul><li>Understand the Content - Content based filtering<br /><ul><li>My knowledge:I have a 3 room, luxury house</li></ul>Understand Users - Collaborative filtering<br /><ul><li>My Experience:If the customer lived in NYC, she will live in NYC
  22. 22. My Experience:One that bought a car is likely to buy a house
  23. 23. My Experience:Customersthatare notmarriedrents</li></li></ul><li>iletken’s award winning social approach<br />
  24. 24. İletken’s Recommendation Technology Solutions Detailed<br />Over 15 Recommendation Algorithms<br /><ul><li>Content Based
  25. 25. Collaborative Based
  26. 26. Social Based</li></ul>Developed , Tweaked & Combined<br />For each spesific business<br /><ul><li>Mobile Operator Recommendations
  27. 27. Music/Video Recommendations
  28. 28. E-Commerce Recommendations</li></li></ul><li>İletken’s Trust Networks<br />Wisdom of the Crowds<br />Circle of Trust<br />
  29. 29. Semi-ExclusiveTrust Networks<br />Let’s ask Keith about music<br />
  30. 30. Semi-ExclusiveTrust Networks<br />Trust each user for a spesific field<br />Let’s ask Keith about politics<br />He might be your expert on music but definetly not politics !<br />
  31. 31. Semi-ExclusiveTrust Networks<br />Different trust networks for different areas of interest<br />Rock and Roll<br />Politics<br />Soccer<br />
  32. 32. Two Collaborative Filtering SystemsExample<br />1. Neighboring based methods<br />2. Matrix Factorization methods<br />
  33. 33. iletken’s Semi-Exclusive Neighbor Algorithm<br />+<br />+<br />+<br />+<br />+<br />+<br />+<br />+<br />+<br />+<br />+<br />+<br />MelodyServicesProximity<br />
  34. 34. iletken’s Semi-Exclusive Neighbor Algorithm<br />+<br />+<br />+<br />+<br />+<br />+<br />+<br />+<br />+<br />+<br />+<br />+<br />Java Games Proximity<br />
  35. 35. iletken’s Matrix Factorization Methods<br />Factor 1<br />Factor 1<br />Factor 2<br />Factor 2<br />Factor 3<br />Factor 3<br />Factor 4<br />Factor 4<br /> Data driven relevancy factors<br />
  36. 36. Thanks<br />Contact<br />Visit<br />Next: Time to contact iletken<br />