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Burke, Mahony, Hurley, Robust Collaborative Recommendation
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http://www.enzyklopaedie-der-wirtschaftsinformatik.de, 1.12.2013
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http://www.enzyklopaedie-der-wirtschaftsinformatik.de, 1.12.2013
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Williams, Mobasher, Burke, Defending Recommender Systems: Detection of Profile Injection Attacks, 2007
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1,80
1,60
1,40
1,20
1,00
0,80
0,60
0,40
0,20
0,00
Random Attack

Bandwagon Attack

Average Attack
25

Burke, Mahony, Hurle...
0,90
0,80
0,70
0,60
0,50
0,40
0,30
0,20
0,10
0,00
All-Users

Segment Attack
26

Burke, Mahony, Hurley, Robust Collaborativ...
0,00
-0,50

Reverse
Bandwagon

Average
Attack

Random
Attack

Bandwagon Love/Hate
Attack
Attack

-1,00
-1,50
-2,00
-2,50

...
0,10
0,00
-0,10
-0,20
-0,30
-0,40
-0,50
-0,60
-0,70
-0,80

Reverse
Bandwagon

Average
Attack

Random
Attack

Bandwagon Lov...
80%
70%
60%
50%
40%
30%
20%
10%
0%
Average Attack

Probe Attack

Popular Attack
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Burke, Mahony, Hurley, Robust Collabor...
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Williams, Mobasher, Burke, Defending Recommender Systems: Detection of Profile Injection Attacks, 2007
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Robustheit in Empfehlungssystemen
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Robustheit in Empfehlungssystemen

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Ein Vortrag von Maximilian Schmidbauer aus dem Hauptseminar "Personalisierung mit großen Daten".

Published in: Technology, News & Politics
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Transcript of "Robustheit in Empfehlungssystemen"

  1. 1.    2
  2. 2.  3 http://de.wikipedia.org/wiki/Robustheit, 1.12.2013
  3. 3.      4
  4. 4. 5
  5. 5.   6
  6. 6.   7
  7. 7.    8 Burke, Mahony, Hurley, Robust Collaborative Recommendation
  8. 8.    9 http://www.enzyklopaedie-der-wirtschaftsinformatik.de, 1.12.2013
  9. 9.    10 http://www.enzyklopaedie-der-wirtschaftsinformatik.de, 1.12.2013
  10. 10.    ∅  11 Williams, Mobasher, Burke, Defending Recommender Systems: Detection of Profile Injection Attacks, 2007
  11. 11.     12
  12. 12.  ∅     13
  13. 13.  ∅      14
  14. 14.         15
  15. 15.      16
  16. 16.     ∅   17
  17. 17.       18
  18. 18.     19
  19. 19.      20
  20. 20.      21
  21. 21.    22
  22. 22.     23
  23. 23.         24
  24. 24. 1,80 1,60 1,40 1,20 1,00 0,80 0,60 0,40 0,20 0,00 Random Attack Bandwagon Attack Average Attack 25 Burke, Mahony, Hurley, Robust Collaborative Recommendation
  25. 25. 0,90 0,80 0,70 0,60 0,50 0,40 0,30 0,20 0,10 0,00 All-Users Segment Attack 26 Burke, Mahony, Hurley, Robust Collaborative Recommendation
  26. 26. 0,00 -0,50 Reverse Bandwagon Average Attack Random Attack Bandwagon Love/Hate Attack Attack -1,00 -1,50 -2,00 -2,50 27 Burke, Mahony, Hurley, Robust Collaborative Recommendation
  27. 27. 0,10 0,00 -0,10 -0,20 -0,30 -0,40 -0,50 -0,60 -0,70 -0,80 Reverse Bandwagon Average Attack Random Attack Bandwagon Love/Hate Attack Attack 28 Burke, Mahony, Hurley, Robust Collaborative Recommendation
  28. 28. 80% 70% 60% 50% 40% 30% 20% 10% 0% Average Attack Probe Attack Popular Attack 29 Burke, Mahony, Hurley, Robust Collaborative Recommendation
  29. 29.       30
  30. 30.     31
  31. 31.     32
  32. 32.       33
  33. 33.   34 Williams, Mobasher, Burke, Defending Recommender Systems: Detection of Profile Injection Attacks, 2007
  34. 34.    35
  35. 35.    36
  36. 36.      37
  37. 37.     38
  38. 38.     39
  39. 39.       40
  40. 40.     41
  41. 41.     42
  42. 42.    43
  43. 43. 44
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