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TRULLO
(daniele quercia & stephen hailes & licia capra)
TRUst bootstrapping by Latently Lifting cOntext
What do I do?
Research @ ...
what I research?
Reputation Systems for Mobiles
What’s that?
Example:antique markets
Problem: Visitors cannotsee prices of everything!
Solution: Sellers disseminate
e-ads, and visitors collect them
Problem: Sellers may disseminate irrelevant ads
Proposal …
Keep track of which sellers send irrelevant ads
A> “May I rely on B?”
Daniele Quercia
But first, A has to set its initial trust in B
Daniele Quercia
Existing Ideas?
Daniele Quercia
3 Solutions …
Daniele Quercia
( over-simplified)fixed values
Daniele Quercia
recommendations ( fake ones)
Daniele Quercia
and inter-context lifting …
Greek Coins
Roman Coins
Coins Chairs
Antiques
universal ontology
Daniele Quercia
Idea behind TRULLO
Daniele Quercia
Users learn from their ratings
Daniele Quercia
They extract “features” from their own ratings
Daniele Quercia
How?
Daniele Quercia
Singular
Value
Decomposition
Daniele Quercia
SVD
Daniele Quercia
personal ratings
(latent) features
unknown value
=combination
of those features
Daniele Quercia
Beauty: features are NOT user-specified BUT learnt
Does it work? User Utility & Time: Fast?
Daniele Quercia
Tested on simulation (realistic?)
(antique)
Daniele Quercia
utility # “Useful” sellers selected
Daniele Quercia
Much knowledge needed?
Daniele Quercia
Daniele Quercia
Time? Tested on …
Daniele Quercia
Nokia 3230
Daniele Quercia
Daniele Quercia
Of course …
Daniele Quercia
Further Research
Daniele Quercia
And if B is unknown?
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