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

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 :

They may keep track of which sellers send irrelevant ads

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

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

Daniele Quercia 3 Existing Solutions

3 Existing Solutions

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

1. Fixed values

(  over-simplified)

Daniele Quercia 2. Recommendations (  fake ones)

2. Recommendations

(  fake ones)

Daniele Quercia 3. Similar contexts (  universal ontology)

3. Similar contexts

(  universal ontology)

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

Daniele Quercia 1. B is unknown

1. B is unknown

Daniele Quercia Popular way: Trust propagation (transitivity) ? A B C

Popular way:

Trust propagation (transitivity)

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

Meant for the Web &

Proved on “binary” ratings

Daniele Quercia Algorithm rating unrated trust relationships (needed) 1 ? A B C 2 1 2 ? unrated nodes (chosen) A  B A  C C  B

Algorithm rating

unrated trust relationships (needed)

unrated nodes (chosen)

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

Idea:

1. Similar nodes together

2. Find function:

same ratings for rated nodes

similar ratings for neighbours

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

Tested on real data

(Advogato: > 55K user ratings)

Daniele Quercia 2. B is partly known

2. B is partly known

Daniele Quercia Popular way: Inter-context Lifting  Greek Coins Roman Coins Coins Chairs Antiques

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

Idea: Users …

> Don’t share ontology

> Extract “features”

from their own ratings

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

Idea: Users …

> Don’t share ontology

> Extract “features”

from their own ratings

Daniele Quercia How to extract?

How to extract?

Daniele Quercia S ingular V alue D ecomposition

S ingular

V alue

D ecomposition

Daniele Quercia Beauty: features not user-specified BUT learnt

Beauty: features

not user-specified

BUT learnt

Daniele Quercia Tested on simulation with real parameters

Tested on simulation with real parameters

Daniele Quercia Tested on Nokia 3230 Max: 3.2 ms !

Tested on Nokia 3230

Max: 3.2 ms !

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

What I’ve told you is on

“ mo bb log UCL ” (google it)

under tag: “ bootstrapping ”

Daniele Quercia

Daniele Quercia And User Privacy?

And User Privacy?

Daniele Quercia Private filtering (Google for “mo bb log private filtering”)

Private filtering

(Google for “mo bb log private filtering”)

Daniele Quercia And Resource Discovery?

And Resource Discovery?

Daniele Quercia Folksonomy for mobiles 

Folksonomy for mobiles 

Daniele Quercia And Attacks?

And Attacks?

Daniele Quercia Further Research (join mobblog !)