Your SlideShare is downloading. ×
0
U   C   L daniele quercia & licia capra & valentina zanardi
I’m doing my PhD @
U niversity C ollege L ondon
<MobiSys>
We   research   distributed sys
We  blog mobblog ucl
Web 2.0 (mobile: $22.4bn)
Location?
The next big thing!
 
$8.1 bn
$8.1 bn
This talk is about  tools for...
Consuming content on the move
Content Creation: By publishers?
People Mainly: people
People Mainly: people
People
 
 
 
create a ...
Digital Tapestry
Creation: Distributed!
Consumption: Centralized!
Why?
Money!
Making Money? No, short of ideas!
Wisdom of the   (Programming)Crowd
API
wired freedom by APIs
 
Creation: Distributed!
What if...
Consume:   centralized    decentralized
 
Existing Tools   for Internet
Tools for   decentralized consumption
<1> Query matching <2> Ratings for sources
<1> Query matching
 
 
 
Centralized Solutions
similarity(query,item)  query item
similarity(query,item)  query item Coverage (digg out content)  + Accuracy (no good content) -
Idea behind SocialRanking [RecSys08]   Social Ranking:Finding Relevant Content in Web 2.0
sim(query,item) +
sim(query,item) + sim(issuer,tagger)
issuer tagger similarity(issuer,tagger)
issuer tagger similarity(issuer,tagger)  Coverage (digg out content)  Accuracy (good content) + +
Future: Decentralized!
<2> Ratings for sources
Store & Use & Categories
Store & Use & Categories
How to store ratings?
1.Log (credentials) 2. Gossip (to check each credential)
1.Log (credentials) 2. Gossip (to check each credential)   Impractical 
Idea behind MobiRate [Ubicomp08]   MobiRate: Making Mobile Raters Stick to their Word
1.Sealed Log (of credentials) 2. Gossip (to check seals only)
1.Sealed Log (of credentials) 2. Gossip (to check seals only)    Practical
<ul><ul><li>works?   </li></ul></ul>
Security: It outperforms existing solutions
“ heaviest” protocol runs < 2sec
“ longest” protocol completed in 2.5ms   (if Bluetooth 100kb/s)
Store & Use & Categories
Use ratings to make predictions
Daniele Quercia <ul><li>Traditional way:   </li></ul><ul><li>Trust propagation </li></ul>? A B C
Daniele Quercia <ul><li>That way works on </li></ul><ul><li>Web & “binary” ratings   </li></ul>
Idea behind LDTP [ICDM07]   Lightweight Distributed Trust Propagation
Daniele Quercia 1 ? A B C 2
Daniele Quercia 1 ? A B C 2 ? new graph f 1 2 A  B A  C C  B
Daniele Quercia 1 ? A B C 2 ? new graph “ good” rating function f 1 2 A  B A  C C  B
<ul><ul><li>works?   </li></ul></ul>
Daniele Quercia Useful?  Tested on real data  (Advogato: > 55K user ratings)
Daniele Quercia Useful?  Tested on real data  (Advogato: > 55K user ratings)
Daniele Quercia Fast and “Light”?
Daniele Quercia Fast and “Light”?   <ul><ul><li>For propagating A  B  </li></ul></ul><ul><ul><li>(worst case) </li></ul><...
Store & Use & Categories
Greek Coins Roman Coins Coins Chairs Antiques universal ontology  
Idea behind TRULLO [MobiQuitous07]   TRULLO - local trust bootstrapping for ubiquitous devices
Daniele Quercia <ul><li>Users  learn  from their ratings </li></ul>
Daniele Quercia <ul><li>Users  learn  from their ratings </li></ul>How?
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
<ul><ul><li>works?   </li></ul></ul>
Daniele Quercia <ul><li>Good “porting” upon few ratings </li></ul>
Daniele Quercia Nokia 3230
Daniele Quercia
Daniele Quercia
Store & Use & Categories
 
Issues: many!
Issue 1: Evaluation
Issue 2: Privacy
If you are looking for   Kinky boots
whether  you  are …
a woman…
or a man a woman…
Thorny problem:
…  or a woman… How to keep it secret
Issue 2: Privacy
 
All this on ...  mobblog ucl
Upcoming SlideShare
Loading in...5
×

Selecting Trustworthy Content Using Tags

1,952

Published on

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

Published in: Technology
0 Comments
1 Like
Statistics
Notes
  • Be the first to comment

No Downloads
Views
Total Views
1,952
On Slideshare
0
From Embeds
0
Number of Embeds
2
Actions
Shares
0
Downloads
21
Comments
0
Likes
1
Embeds 0
No embeds

No notes for slide

Transcript of "Selecting Trustworthy Content Using Tags"

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

    Clipping is a handy way to collect important slides you want to go back to later.

×