iletken recommendation technologies iletken tavsiye sistemleri tanıtım

1. Social Recommendation Technologies

2. Recommending items of interest to users based on explicit or implicit preferneces Problem? 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. Alex Iskold, ReadWriteWeb 2007

3. User Frustration… …Lost Business Opportunity

4. with Increase Usage and Sales between %10-50 by connecting the right content the right user * iletken for Mobile Content Recommendations slide

5. You Need To Understand the User Understand the Content For Giving Right Content to the Right User

6. Content Social & User Network User action iletken Recommender System Interactions Content and Context Customized Solution Business Client Analytics and Feedback Real Time Recommendations

7. Benefits Monetize Niche Content The bottom line is… Generate Cross Sales Increase Usability Sales Increase 10% - 50% Better Customer Service Targeted Reach … and more

8. Awards and Global Recognition 3rd best recommender startup at ACM’s RecSys’08… … out of 26 projects from 15 countries worldwide “GeleceğıninternetindeTürkimzası.” CNN Türk ’08 “One of 5 early recommendation technologies that could shake up their niches.” ReadWriteWeb ‘08 iletken is a proud software partner of intel iletken R&D is supported by TÜBİTAK

9. Our Hybrid Technology Behavior based Content based Social Relevancybased Context based proximity graphs Natural language processing Collaborative filtering Metadata analysis MachineLearning vs

10. About iletken Technologies

11. iletken for Media Content Recommendations

12. iletken for Mobile Content Recommendations Personalized targeting for… Life – Ukraine results … mobile game downloads and melodies %331 Elevation on Niche Content %411 Elevation on Popular Content Overall %35-50 increase in subscription

13. iletken for E-Commerce Recommendations

17. Next: More on RecommendationTechnologies 1. Real WorldExample: Salesman 2. RecommendationMethodsDetailed

18. The Salesman Analogy

20. Needs to know about the buyers

22. My Experience:One that bought a car is likely to buy a house

25. Collaborative Based

27. Music/Video Recommendations

29. Semi-ExclusiveTrust Networks Let’s ask Keith about music

30. Semi-ExclusiveTrust Networks Trust each user for a spesific field Let’s ask Keith about politics He might be your expert on music but definetly not politics !

31. Semi-ExclusiveTrust Networks Different trust networks for different areas of interest Rock and Roll Politics Soccer

32. Two Collaborative Filtering SystemsExample 1. Neighboring based methods 2. Matrix Factorization methods

33. iletken’s Semi-Exclusive Neighbor Algorithm + + + + + + + + + + + + MelodyServicesProximity

34. iletken’s Semi-Exclusive Neighbor Algorithm + + + + + + + + + + + + Java Games Proximity

35. iletken’s Matrix Factorization Methods Factor 1 Factor 1 Factor 2 Factor 2 Factor 3 Factor 3 Factor 4 Factor 4 Data driven relevancy factors

36. Thanks Contact info@iletken-project.com Visit http://www.iletken-project.com/ Next: Time to contact iletken