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Selecting Trustworthy Content Using Tags

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How to offer digital content to mobile users by combining tagging with reputation systems

How to offer digital content to mobile users by combining tagging with reputation systems

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  • 1. U C L daniele quercia & licia capra & valentina zanardi
  • 2. I’m doing my PhD @
  • 3. U niversity C ollege L ondon
  • 4. <MobiSys>
  • 5. We research distributed sys
  • 6. We blog mobblog ucl
  • 7. Web 2.0 (mobile: $22.4bn)
  • 8. Location?
  • 9. The next big thing!
  • 10.  
  • 11. $8.1 bn
  • 12. $8.1 bn
  • 13. This talk is about tools for...
  • 14. Consuming content on the move
  • 15. Content Creation: By publishers?
  • 16. People Mainly: people
  • 17. People Mainly: people
  • 18. People
  • 19.  
  • 20.  
  • 21.  
  • 22. create a ...
  • 23. Digital Tapestry
  • 24. Creation: Distributed!
  • 25. Consumption: Centralized!
  • 26. Why?
  • 27. Money!
  • 28. Making Money? No, short of ideas!
  • 29. Wisdom of the (Programming)Crowd
  • 30. API
  • 31. wired freedom by APIs
  • 32.  
  • 33. Creation: Distributed!
  • 34. What if...
  • 35. Consume: centralized  decentralized
  • 36.  
  • 37. Existing Tools for Internet
  • 38. Tools for decentralized consumption
  • 39. <1> Query matching <2> Ratings for sources
  • 40. <1> Query matching
  • 41.  
  • 42.  
  • 43.  
  • 44. Centralized Solutions
  • 45. similarity(query,item) query item
  • 46. similarity(query,item) query item Coverage (digg out content) + Accuracy (no good content) -
  • 47. Idea behind SocialRanking [RecSys08] Social Ranking:Finding Relevant Content in Web 2.0
  • 48. sim(query,item) +
  • 49. sim(query,item) + sim(issuer,tagger)
  • 50. issuer tagger similarity(issuer,tagger)
  • 51. issuer tagger similarity(issuer,tagger) Coverage (digg out content) Accuracy (good content) + +
  • 52. Future: Decentralized!
  • 53. <2> Ratings for sources
  • 54. Store & Use & Categories
  • 55. Store & Use & Categories
  • 56. How to store ratings?
  • 57. 1.Log (credentials) 2. Gossip (to check each credential)
  • 58. 1.Log (credentials) 2. Gossip (to check each credential)   Impractical 
  • 59. Idea behind MobiRate [Ubicomp08] MobiRate: Making Mobile Raters Stick to their Word
  • 60. 1.Sealed Log (of credentials) 2. Gossip (to check seals only)
  • 61. 1.Sealed Log (of credentials) 2. Gossip (to check seals only)    Practical
  • 62.
      • works?
  • 63. Security: It outperforms existing solutions
  • 64. “ heaviest” protocol runs < 2sec
  • 65. “ longest” protocol completed in 2.5ms (if Bluetooth 100kb/s)
  • 66. Store & Use & Categories
  • 67. Use ratings to make predictions
  • 68. Daniele Quercia
    • Traditional way:
    • Trust propagation
    ? A B C
  • 69. Daniele Quercia
    • That way works on
    • Web & “binary” ratings
  • 70. Idea behind LDTP [ICDM07] Lightweight Distributed Trust Propagation
  • 71. Daniele Quercia 1 ? A B C 2
  • 72. Daniele Quercia 1 ? A B C 2 ? new graph f 1 2 A  B A  C C  B
  • 73. Daniele Quercia 1 ? A B C 2 ? new graph “ good” rating function f 1 2 A  B A  C C  B
  • 74.
      • works?
  • 75. Daniele Quercia Useful? Tested on real data (Advogato: > 55K user ratings)
  • 76. Daniele Quercia Useful? Tested on real data (Advogato: > 55K user ratings)
  • 77. Daniele Quercia Fast and “Light”?
  • 78. Daniele Quercia Fast and “Light”?
      • For propagating A  B
      • (worst case)
      • Transmit 30KB
      • & run for 2.8ms
  • 79. Store & Use & Categories
  • 80. Greek Coins Roman Coins Coins Chairs Antiques universal ontology 
  • 81. Idea behind TRULLO [MobiQuitous07] TRULLO - local trust bootstrapping for ubiquitous devices
  • 82. Daniele Quercia
    • Users learn from their ratings
  • 83. Daniele Quercia
    • Users learn from their ratings
    How?
  • 84. Daniele Quercia
    • S ingular
      • V alue
      • D ecomposition
  • 85. Daniele Quercia SVD
  • 86.
      • works?
  • 87. Daniele Quercia
    • Good “porting” upon few ratings
  • 88. Daniele Quercia Nokia 3230
  • 89. Daniele Quercia
  • 90. Daniele Quercia
  • 91. Store & Use & Categories
  • 92.  
  • 93. Issues: many!
  • 94. Issue 1: Evaluation
  • 95. Issue 2: Privacy
  • 96. If you are looking for Kinky boots
  • 97. whether you are …
  • 98. a woman…
  • 99. or a man a woman…
  • 100. Thorny problem:
  • 101. … or a woman… How to keep it secret
  • 102. Issue 2: Privacy
  • 103.  
  • 104. All this on ... mobblog ucl