controversial users demand local trust metrics: an experimental study on epinions.com community
DESCRIPTION
Presentation at AAAI 2005 (American Association for Artificial Intelligence).TRANSCRIPT
Controversial Users demandLocal Trust Metrics: an
ExperimentalStudy on Epinions.com
Community
Paolo MassaPhD student in ICT
ITC/iRST and University of TrentoBlog: http://moloko.itc.it/paoloblog/
(Joint Work with Paolo Avesani)(Thanks Epinions.com for providing data)
Slides licenced under CreativeCommons AttributionShareAlike (see last slide for more info)
Summary
■What is Epinions.com■Trust Networks and Trust Metrics
Local vs Global Trust Metrics■Controversial Users■Experiments and Results■Conclusions
Motivation In a society, some peers are unknown to you (ebay,
p2p, ...) Q: “Should I trust peer A?” [decentralization>relevant]
Most papers assume a peer has a unique quality value (there are good peers and bad peers, goal is to spot bad)
IRREALISTIC assumption (Evidence from real online community of 150.000 users).
Consequence: we need Local Trust Metrics (personalized) [But most papers propose Global Metrics]
Epinions.com
What is Epinions.com? Community web site where users can
Write reviews about items and give them ratings Express their Web of Trust (“Users whose reviews and
ratings you have consistently found to be valuable”) Express their Block List (“Users whose reviews and
ratings ... offensive, inaccurate, or in general not valuable”)
Reviews of TRUSTed users are more visible Reviews of DISTRUSTed users are hidden
Dr.P profile page
Ratings given by Dr.P
Dr.P's Web of Trust(Block List is hidden)
Do you trust or distrust Dr.P?
Epinions.com
Real uses of Trust
News sites: Slashdot.org, Kuro5hin.org, ...
Emarketplaces: Ebay.com, Epinions.com, Amazon.com, ...
P2P networks: eDonkey, Gnutella, JXTA
Jobs sites: LinkedIn, Ryze, ...
Friendster, Tribes, Orkut and other “social” sites.
Opensource Developers communities: Advogato.org (Affero.org)
Hospitalityclub.org, couchsurfing.com: hosting in your house unknown people?
Bookcrossing and lending stuff sites.
Network of personal weblogs (the blogroll is your trust list)
Semantic Web: FOAF (FriendOfAFriend) is an RDF format that allows to express social relationships (~10 millions files) and XFN microformat
PageRank (Google) ... MyWeb2.0 (Yahoo!)
Trust networks (are graphs) Aggregate all the trust statements to produce a
trust network.
Dr.P
Mena
0.9
Doc1
00.2
?
Ben
0.6 weighted (0=distrust, 1=max trust)
asymmetric subjective
A node is a user (example: Dr.P).
Properties of Trust:
A direct edge is a trust statement
Trust Metric (TM):Uses existing edges for predicting valuesof trust for nonexisting edges, thanks to trust propagation (if you trust someone, then you have some degree of trust in anyone that person trusts).
contextdependent
In Epinions, just 1 and 0!
TM perspective: Local or Global
Global Trust Metrics: “Reputation” of user is based on number and quality of incoming edges. Bill has
just one predicted trust value (0.5). PageRank (Google), eBay, Slashdot, ... Works badly for controversial people
Local Trust Metrics Trust is subjective > consider personal views (trust “Bill”?) Local can be more effective if people are not standardized.
ME
Mena
Doc1
Mary BillHow much Bill can be trusted? On average (by the community)? By Mary? And by ME?
1 1
0
Controversial Users
Intuitively: a Controversial User is TRUSTED by some users DISTRUSTED by some users
Do you want an example?
Controversial Users: an example
11
11
1
1(....)
100M people
0
00
0
00
(....)
100M people
If you don't know Bush, should you trust Bush?
T(Bush)=0.5? Make sense? Here global metrics don't.
Controversial Users: an example
11
11
1
1(....)
100M people
0
00
0
00
(....)
100Mpeople
Local Metric makes more sense. Your trust in Bush depends on your trusted users!
T(R,Bush)=1 T(D,Bush)=0
R 1
1D
11
1
Controversial Users on Epinions Controversial users are normal in societies
How many controversial users on Epinions.com?
But first, two definitions of Controversiality: Controversiality Level of A: number of users that
disagree with the majority = Min(#trust, #distrust) Contr. “Percentage” of A = (TD) / (T+D) in [1, 1]
CP(A)=1 if A is trusted by everyone (loved!) CP(A)=1 if A is distrusted by everyone (hated!) CP(A)=0 if A is trusted by n users and distrusted by n users
Experiment Epinions.com dataset
Real Users: ~150K Edges (Trust / Distrust): 841K (717K / 124K)
~85K received at least one judgement (trust or distrust) 17.090 (>20%) are at least 1controversial (at least 1 user
disagrees with the majority) > Non negligible portion! 1.247 are at least 10controversial 144 are at least 40controversial 1 user is 212controversial! (~400 trust her, 212 distrust her)
Experiment Comparing 2 metrics about accuracy in trust/distrust
prediction. Global: ebaylike. Trust(A)=#trust/(#trust+#distrust) Local: MoleTrust, based on Trust Propagation from current
user (simple and fast)
Cycles are a problem > Order peers based on distance from source userTrust of users at level k is based only
on trust of users at level k1 (and k)Trust propagation horizon & decay
How do we compare metrics? Leaveoneout: Remove an edge in Trust Network and
try to predict it. Then compute error as absolute difference between Real and Predicted value. Also differentiating over trust or distrust statements
Experiment
Exp. on Controversiality Level
Ebay Controversiality level
MoleTrust2 Controversiality level
Erro
rEr
ror
y=error made by TM predicting edges on users with x controversiality level.
Predicting Distrust is more difficult.
Ebay error on Distrust ~ 0.6
Mole2 error on Distrust ~ 0.4
Error on Trust is similar because (#trust >> #distrust)
Exp. on Controversiality Percentage
Ebay Controversiality percentage
MoleTrust2 Controversiality percentage
Erro
rEr
ror
CP~0 = Controversial User
Error Ebay = 0.5 on Contr.Us
Error MoleTrust2 smaller but not as small as we would like: can we reach 0?
Other experiments in paper:Error on Trust Edges.
Error on Distrust Edges (very important to correctly predict these ones!)
Other experiments
MoleTrust with different propagation horizons 2, 3, 4
Computing Coverage.
Conclusions In complex societies, it is normal that someone likes
you and someone dislikes you. Most Papers make assumption of unique quality
value for a peer (and propose an algo for predicting it) This is IRREALISTIC! (I know this is intuitive but
still ...)
Conclusions (2) As a consequence, we need Local Trust Metrics.
Local TMs are computationally much more expensive than Global TMs! > Possibly, you run it locally for yourself on your mobile or on your browser (should be fast!)
Local TMs exploits less information > reduced coverage. Global Metrics fine in noncontroversial domains: possibly
ok on Ebay, surely not ok on sites about (political?) opinions Trust networks are everywhere! More research is needed: Yahoo! (with MyWeb2.0)
and Google are there. More real testbeds, more proposals of Local TMs, more comparisons, ...
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The End
The End.Thanks for your attention!
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