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Inter-Context Trust Bootstrapping for Mobile Content Sharing (daniele quercia) (stephen hailes & licia capra) U C LU C L.

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Presentation on theme: "Inter-Context Trust Bootstrapping for Mobile Content Sharing (daniele quercia) (stephen hailes & licia capra) U C LU C L."— Presentation transcript:

1 Inter-Context Trust Bootstrapping for Mobile Content Sharing (daniele quercia) (stephen hailes & licia capra) U C LU C L

2 What do I do?

3 Research @

4 what I research?

5 Reputation Systems for Mobiles

6 What’s that?

7 Example: antique markets

8 Problem: Visitors cannot see prices of everything!

9 Solution: Sellers disseminate e-ads, and visitors collect them

10 Problem: Sellers may disseminate irrelevant ads

11 Proposal:

12 They may keep track of which sellers send irrelevant ads

13 Daniele Quercia Trust model on A: how A decides whether to rely on B to visit a stall

14 Daniele Quercia To decide whether to rely on B, A has to set its initial trust in B

15 Daniele Quercia 3 Existing Solutions

16 Daniele Quercia 1. Fixed values (  over-simplified)

17 Daniele Quercia 2. Recommendations (  fake ones)

18 Daniele Quercia 3. Similar contexts (  universal ontology)

19 Daniele Quercia Two cases: B is 1. unknown 2. partly known

20 Daniele Quercia 1. B is unknown

21 Daniele Quercia Popular way: Trust propagation (transitivity) ? AB C

22 Daniele Quercia  Meant for the Web & Proved on “binary” ratings

23 Daniele Quercia Algorithm rating unrated trust relationships (needed) 1 ? AB C 2 12 ? unrated nodes (chosen) ABAB ACACCBCB

24 ? Idea: 1. Similar nodes together 2. Find function: same ratings for rated nodes similar ratings for neighbours

25 Daniele Quercia Tested on real data (Advogato: > 55K user ratings)

26 Daniele Quercia 2. B is partly known

27 Daniele Quercia Popular way: Inter-context Lifting Greek Coins Roman Coins CoinsChairs Antiques

28 Daniele Quercia Idea: Users … > Don’t share ontology > Extract “features” from their own ratings

29 Daniele Quercia Idea: Users … > Don’t share ontology > Extract “features” from their own ratings

30 Daniele Quercia How to extract?

31 Daniele Quercia S ingular V alue D ecomposition

32 Daniele Quercia Beauty: features not user-specified BUT learnt

33 Daniele Quercia Tested on simulation with real parameters

34 Daniele Quercia Tested on Nokia 3230 Max: 3.2 ms !

35 Daniele Quercia What I’ve told you is on “mobblog UCL” (google it) under tag: “bootstrapping”

36 Daniele Quercia

37 And User Privacy?

38 Daniele Quercia Private filtering (Google for “mobblog private filtering”)

39 Daniele Quercia And Resource Discovery?

40 Daniele Quercia Folksonomy for mobiles

41 Daniele Quercia And Attacks?

42 Daniele Quercia Further Research (join mobblog !)


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