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TRULLO (daniele quercia & stephen hailes & licia capra) U   C   L
U   C   L
TRU st bootstrapping by  L atently  L ifting c O ntext
What do I do?
Research @ ...
 
what I research?
Reputation Systems for Mobiles
What’s that?
Example: antique markets
Problem:   Visitors  cannot see prices of everything!
Solution:   Sellers  disseminate  e-ads, and  visitors  collect them
Problem:  Sellers may  disseminate irrelevant ads
Proposal …
Keep track of which sellers  send irrelevant ads   A > “May I rely on  B ?”
Daniele Quercia But first,  A  has to set   its initial trust in  B
Daniele Quercia <ul><ul><li>Existing Ideas?   </li></ul></ul>
Daniele Quercia <ul><ul><li>3  Solutions …   </li></ul></ul>
Daniele Quercia <ul><ul><ul><li>(   over-simplified) </li></ul></ul></ul>fixed values
Daniele Quercia <ul><ul><ul><li>recommendations </li></ul></ul></ul><ul><ul><ul><li>(   fake ones) </li></ul></ul></ul>
Daniele Quercia <ul><ul><ul><li>and inter-context lifting … </li></ul></ul></ul>
Greek Coins Roman Coins Coins Chairs Antiques universal ontology  
Daniele Quercia <ul><ul><li>Idea  behind TRULLO   </li></ul></ul>
Daniele Quercia <ul><ul><li>Users  learn  from their ratings </li></ul></ul>
Daniele Quercia <ul><ul><li>They extract “features”  </li></ul></ul><ul><ul><li>from their own ratings </li></ul></ul>
Daniele Quercia <ul><ul><li>How? </li></ul></ul>
Daniele Quercia <ul><li>S ingular </li></ul><ul><ul><li>V alue </li></ul></ul><ul><ul><li>D ecomposition </li></ul></ul>
Daniele Quercia SVD
Daniele Quercia personal ratings (latent) features   unknown value = combination of those features
Daniele Quercia <ul><ul><li>Beauty:  features are   </li></ul></ul><ul><ul><li>NOT user-specified BUT learnt </li></ul></ul>
<ul><li>Does it work?   </li></ul><ul><li>User Utility  &  Time: Fast? </li></ul>
Daniele Quercia Tested on simulation  (realistic?) <ul><ul><ul><li>(antique) </li></ul></ul></ul>
Daniele Quercia <ul><ul><ul><li>   utility       # “Useful” sellers selected </li></ul></ul></ul>
Daniele Quercia <ul><li>Much knowledge needed?   </li></ul>
Daniele Quercia
Daniele Quercia <ul><li>Time? Tested on …   </li></ul>
Daniele Quercia Nokia 3230
Daniele Quercia
Daniele Quercia <ul><li>Of course … </li></ul>
Daniele Quercia Further Research
Daniele Quercia <ul><ul><ul><li>And if  B  is unknown? </li></ul></ul></ul>
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TRULLO - local trust bootstrapping

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Transcript of "TRULLO - local trust bootstrapping"

  1. 1. TRULLO (daniele quercia & stephen hailes & licia capra) U C L
  2. 2. U C L
  3. 3. TRU st bootstrapping by L atently L ifting c O ntext
  4. 4. What do I do?
  5. 5. Research @ ...
  6. 7. what I research?
  7. 8. Reputation Systems for Mobiles
  8. 9. What’s that?
  9. 10. Example: antique markets
  10. 11. Problem: Visitors cannot see prices of everything!
  11. 12. Solution: Sellers disseminate e-ads, and visitors collect them
  12. 13. Problem: Sellers may disseminate irrelevant ads
  13. 14. Proposal …
  14. 15. Keep track of which sellers send irrelevant ads A > “May I rely on B ?”
  15. 16. Daniele Quercia But first, A has to set its initial trust in B
  16. 17. Daniele Quercia <ul><ul><li>Existing Ideas? </li></ul></ul>
  17. 18. Daniele Quercia <ul><ul><li>3 Solutions … </li></ul></ul>
  18. 19. Daniele Quercia <ul><ul><ul><li>(  over-simplified) </li></ul></ul></ul>fixed values
  19. 20. Daniele Quercia <ul><ul><ul><li>recommendations </li></ul></ul></ul><ul><ul><ul><li>(  fake ones) </li></ul></ul></ul>
  20. 21. Daniele Quercia <ul><ul><ul><li>and inter-context lifting … </li></ul></ul></ul>
  21. 22. Greek Coins Roman Coins Coins Chairs Antiques universal ontology 
  22. 23. Daniele Quercia <ul><ul><li>Idea behind TRULLO </li></ul></ul>
  23. 24. Daniele Quercia <ul><ul><li>Users learn from their ratings </li></ul></ul>
  24. 25. Daniele Quercia <ul><ul><li>They extract “features” </li></ul></ul><ul><ul><li>from their own ratings </li></ul></ul>
  25. 26. Daniele Quercia <ul><ul><li>How? </li></ul></ul>
  26. 27. Daniele Quercia <ul><li>S ingular </li></ul><ul><ul><li>V alue </li></ul></ul><ul><ul><li>D ecomposition </li></ul></ul>
  27. 28. Daniele Quercia SVD
  28. 29. Daniele Quercia personal ratings (latent) features   unknown value = combination of those features
  29. 30. Daniele Quercia <ul><ul><li>Beauty: features are </li></ul></ul><ul><ul><li>NOT user-specified BUT learnt </li></ul></ul>
  30. 31. <ul><li>Does it work? </li></ul><ul><li>User Utility & Time: Fast? </li></ul>
  31. 32. Daniele Quercia Tested on simulation (realistic?) <ul><ul><ul><li>(antique) </li></ul></ul></ul>
  32. 33. Daniele Quercia <ul><ul><ul><li> utility   # “Useful” sellers selected </li></ul></ul></ul>
  33. 34. Daniele Quercia <ul><li>Much knowledge needed? </li></ul>
  34. 35. Daniele Quercia
  35. 36. Daniele Quercia <ul><li>Time? Tested on … </li></ul>
  36. 37. Daniele Quercia Nokia 3230
  37. 38. Daniele Quercia
  38. 39. Daniele Quercia <ul><li>Of course … </li></ul>
  39. 40. Daniele Quercia Further Research
  40. 41. Daniele Quercia <ul><ul><ul><li>And if B is unknown? </li></ul></ul></ul>
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