Lecture about trust in Recommender Systems (2 hours) given at the University of Udine in 2006

1. Trust in Recommender Systems
a historical overview and recent
developments
Paolo Massa
Universita' di Trento e ITC/iRST
http://moloko.itc.it/paoloblog/
(adapted by Hassan Masum)
Slides licenced under CreativeCommons Attribution­ShareAlike (see last slide for more info) 1

2. Ask and ye shall receive...
There are no stupid questions.
Interrupt me and ask!
2

3. Plan of the talk
■ Info Overload
■ Info Retrieval vs Info Filtering
■ Content­based Filtering

Weaknesses
■ Collaborative Filtering (aka Recommender
Systems)

Weaknesses
■ Trust­aware Filtering

What is trust? Reputation? 3

4. Info Overload
■ 5 seconds
■ 1 ... 2 ... 3 ... 4 ... 5
4

5. Info Overload
■ 5 seconds:

Scientific information written in this 5 seconds can keep you
busy reading for 40 minutes (based on 1985 data!)

400KB of new text published on paper (24TB printed each
year, 2000, “How Much Information” project at Berkeley)

You have received an email (probably spam) ;­)
■ Is this true? Who can tell .... Take facts with
a grain of salt.
■ quot;Technology reduces the amount of time it takes to do any one
task but also leads to the expansion of tasks that people are
expected to do.quot; ­­ Juliet Schor 5

6. Info Overload (IO)
■ IO refers to the state of having too much
information to make a decision or remain
informed about a topic.
■ The term was coined in 1970 by Alvin Toffler
in his book “Future Shock.”
■ http://en.wikipedia.org/wiki/Information_overload
■ Too much information can be worse than too little –
illusion of being informed
6

7. Info Overload Stats
■ (NO NO – I'm not reading it, it is just a practical example of information overload!)
■ The daily New York Times now contains more information that the 17th century man or woman would have encountered in a
lifetime. (Wurman, S.A. (1987) Information Anxiety. New York: Doubleday, 32.)
■ quot;As we go from grade school to high school we learn only a billionth of what there is to learn. There is enough scientific information written every
day to fill seven complete sets of Encyclopedia Britannica; there is enough scientific information written every year to keep a person busy
reading day and night for 460 years!quot; (Siegel, B.L. (1984, April 15). Knowledge with commitment: Teaching is the central task of the
university. Vital Speeches of the Day, 50, 394.)
■ quot;In the last 30 years mankind has produced more information than in the previous 5,000.quot; (Information Overload Causes Stress. (1997,
March/April). Reuters Magazine. Available: Lexis Nexis Universe [4/28/98].)
■ Gordon Moore, co­founder of Intel, coined Moore's Law which states that the processing power of computer chips doubles about every 18 months.
■ quot;About 1,000 books are published internationally every day, and the total of all printed knowledge doubles every five years.
(Information Overload Causes Stress. (1997, March/April). Reuters Magazine. Available: Lexis Nexis Universe [4/28/98].)
■ quot;The average Fortune 1000 worker already is sending and receiving approximately 178 messages and documents each day, according to a recent
study, quot;Managing Corporate Communications in the Information Age.quot; (Boles, M. (1997) Help! Information overload. Workforce, 76, 20.)
■ quot;Dr Dharma Singh Khalsa, in his book Brain Longevity,...says the average American sees 16,000 advertisements, logos, and labels in a day.quot; (Gore,
A. (1998, January 18) . Stressed? Maybe it's information overload. Sun Herald, 27.)
■ University of California Berkely has a quot;How Much Informationquot; project which studies the amount of information produced each year. quot;The world's
total yearly production of print, film, optical, and magnetic content would require roughly 1.5 billion gigabytes of storage. This is the equivalent of
250 megabytes per person for each man, woman, and child on earth.quot; Berkeley: How Much Information?
(http://www.sims.berkeley.edu/research/projects/how­much­info/)
■ http://library.humboldt.edu/~ccm/fingertips/ioverloadstats.html
■ http://www.sims.berkeley.edu/research/projects/how­much­info­2003/execsum.htm#summary
■ Data Smog: Surviving the Information Glut, by David Shenk 7

8. Info overload
■ BLOGS!!!
■ Am I contributing? You bet :­)

You now have to do some Information Retrieval /
Filtering
8

9. Info Retrieval vs Info Filtering
■ Info Retrieval: deals with static information
(Reuters, a database, a book): you want to find
information that is “lying there”
■ Info Filtering: deals with dynamic information
(flows such as the Web or the media): you want to
prioritize important incoming information, and
block the rest
■ Relevance and Quality of items

On a paper repository like Citeseer: no papers about “spam”
but good papers about “spam”

Which “spam” papers are worth your while? 9

10. Recommender Systems
■ Algorithms/systems that suggest to a user
items she might like.
■ Books, Songs, Restaurants, Food, ...,
Jokes, ..., anything?
■ E­commerce sites (but not only!)

For now, think of Amazon.com
10

11. Recommender Systems
Techniques:
■ Content­based
■ Collaborative Filtering (CF)
■ Trust­aware
[... always think of a way of “spamming” the
technique I describe. It is a safe assumption
nowadays...] 11

12. Content­Based RSs
●
RSs find items similar to ones you liked in past. How?
Analyse the “syntactic content” of all the items.
●
Example: If you like papers containing word “Info
Retrieval”, RS recommends to you another paper with the
word “Info Retrieval” in it.
●
If you read news containing word “Darfur”, it recommends
to you other news with the word “Darfur”.
●
If you like movies of Kubrik, you get one more movie of
Kubrik.
●
Techniques of Info Retrieval ...
.... What are the weaknesses? STOP!
12

13. Content­Based RSs weaknesses
●
Good for text: If you like papers containing word
“Info Retrieval”, RS recommends you another
paper with the word “Info Retrieval” in it. (And
partially effective ways to find “similar” papers ­
vector space, LSI.)
●
For movies or songs, humans must tag the
content (genre, actors, year, ...) but this is time­
consuming, costly, errors­prone and subjective.
– Can your employees “correctly” tag all the
podcasts? All the videos? All the photos? 13

14. Content­Based RSs weaknesses
Content­based RSs weaknesses summary:
●
Text Items (papers, news): Doable but RSs tend
to propose always the same soup (boring).
Difficult to recognize synonyms, concepts, or
new emerging words (such as “folksonomy”).
●
Movies or Songs: not parsable at the moment by
machines, so humans must tag them.
●
Jokes (or subjective items such as political
ideas): What are the “right” features? Tagging
“objectively” is not possible! 14

15. Collaborative Filtering
●
Users give ratings to items (implicit or
explicit)
●
I like “Titanic” as 4/5
●
RS finds users similar to you (User
similarity)
●
Suggests to you items liked by similar user
Idea: out there, there is someone that is similar to
you and you will like what they liked. 15

16. 1
Item2
4
Item3
Item
Item
Ratings from 1 (min) to 5 (max)
ME 2 5 ? 5
2 5 5 5
Sim(ME,User2) = ­0,2 User2 5 1 3
Sim(ME,User3) = ­0,4 User3 5 5 1
Sim(ME,User4) = +0,9 User4 2
2 5
5 5
5 4
4
It does not consider the content of the items, only
the ratings given by users.
It works independently of the domain (also jokes)
BUT
Overlapping of rated items required!
16

17. Let's collect a real example
■ We collect some ratings for “our” RS.
■ Movies: Matrix, LOTR, Hotel Rwanda,
Titanic, La vita e' bella.
17

18. CF formulas
Similarity measure: Pearson Correlation
Coefficient of user a and u (in [­1,+1])
m
∑i=1  r a ,i −r a  r u , i −r u 
wa , u =
∑ m
i=1
r a ,i −r a 
2 m 2
∑i =1  r u , i−r u 
Prediction of rating given by user a to item i
n
∑u=1  r u ,i − r u ∗wa , u

p a , i =r a 
 n
∑u=1 w a , u 18

19. CF pros
CF is Simple and effective,
Works for every kind of item independently
of the domain (ex: Jester recommends
jokes).
It allows Serendipity (fortunate discoveries):
you get recommended “Iron 3” even if you
never saw a Korean movie.
BUT ...
19

20. CF WEAKNESSES!!!
■ User Similarity often not computable
– Ratings Matrix sparseness (95­99%) ­> Low or No
overlapping
■ Cold start
– New users have 0 ratings (­> not comparable)
– At the beginning, your RS is not Amazon!
■ Easy Attacks by Malicious Users
– Copy profile and become the most similar
– Even easier on the Semantic Web
■ Hard to understand and control
– Black box (bad recs ­> user gives up)
Solution? Trust! 20

21. Trust­awareness
Trust: explicit rating of a user on another user
●
about the perceived quality of the user's
characteristics
●
in RSs, you “trust” someone if you like her
tastes
­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­
We will now speak about trust and trust metrics
and then we will come back to “trust and RSs” 21

22. Trust usage for real
E­marketplaces: Ebay.com, Epinions.com, Amazon.com
News sites: Slashdot.org, Kuro5hin.org
P2P networks: eDonkey, Gnutella, JXTA
Job sites: LinkedIn, Ryze, ...
Open Source Developer communities: Advogato.org (Affero.org)
Hospitalityclub, couchsurfing: hosting in your house unknown people?
Bookcrossing and lending stuff sites
Network of personal weblogs (millions of blogrolls!)
Semantic Web: FOAF (Friend­Of­A­Friend) is an RDF format that allows
you to express social relationships (~10 million files)
Google (and Yahoo!): TrustRank (patented)
22

23. Trust networks
■ Aggregate all the trust statements to produce a
trust network. A node is a user.
A direct edge is a trust statement
0
Mena Ben
0.2 Properties of Trust:
0.9 ­ weighted (0=distrust, 1=max trust)
0.6
­ subjective
1
ME Doc ­ asymmetric ­ context­dependent?
Trust Metric (TM):
? ? Uses existing edges for predicting values
of trust for non­existing edges.
1 Thanks to trust propagation, if you trust
Cory Mary
someone, then you have some degree of
trust in anyone that person trusts. 23

24. PageRank: a trust metric?
Imagine the web as a ■ Nodes are web pages, Edges
trust network are links (not weighted).
Web
■ PageRank
Web
page (Google)computes the
page
“importance” of every single
page based on number and
Web Web
page quality of incoming edges...
page
■ So, YES: PageRank is a
trust metric.
Web Web ■ HITS as well.
page page
24

25. Spam a Trust Metric
■ Is it easy to spam a trust metric?
■ It depends: some are attack­resistant
(advogato, for example)
■ Identity is an issue
■ ... and “social cost of cheap pseudonyms” by
Eric Friedman and Paul Resnick: unknown
peers should not be trusted.
25

26. TM perspective: Local or Global
1 1
Mary Mena Bill
How much can Bill be trusted?
0 On average (by the community)?
ME 1 By Mary?
Doc
And by ME?
■ 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 (eBay, Slashdot, ). Work bad for controversial people (bush)
■ Local Trust Metrics

Trust is subjective ­­> consider personal views (trust “Bill”?)

AppleSeed, Golbeck TM, Advogato, ...

Local can be more effective if people are not standardized. 26

27. Local vs Global
■ Search engine: abortion, jew, scientology, ...
■ Who can define what is spam? Google? A site that opposes
Chinese Comm. Party should be removed?
■ Local vs global:

Is gwbush.com a good page? Is johnkerry.com a good page? Is
sex.com a good page?
■ Maybe these questions are meaningless?

It depends on YOUR LOCAL point of view!

republican/democrats, child/parent, federal/newglobal,
catholic/atheist, pro/against abortion, ...
■ Tyranny of the majority / Daily Me (Sunstein)
27

28. Sociology and Trust
■ Is this Sociology?
■ Yes ...
■ You have seen many graphs, but the first to model groups
in this way was Moreno, a sociologist (1934, sociogram).
■ Social network analysis
(faculty.ucr.edu/~hanneman/nettext/)
■ Degree, betweeness, centrality, ...
■ Is this Politics? Yes ...
■ Read “Republic.com” and “Why Societies Need Dissent” by
Cass Sunstein 28

29. Economy and Trust
■ Is this Economy? Yes...
■ Reputation is an asset, for companies (marketing) but
also for people
■ Centrality in Network is money as well.
■ Open source movement: your peers knows you and will
hire you when they need someone they trust and value.
But also researchers (who gets the next Nobel in
Physics? The most “trusted” by physicists!)
■ Read “Down and out in the Magic Kingdom” ­ SciFi,
reputation (whuffie) is the only currency
29

30. Trust and Search Engines
■ 3 generation search engines:
rd
■ personalization of results based on trust networks
(LOCAL!), based on what your friends like/dislike.
■ Google and Yahoo! are moving in this direction (I'm
speculating). [TrustRank]
■ Problem: Scalability! You cannot recompute
PageRank of every site for every user!
■ But you can do it on your laptop/mobile for
yourself, aggregating only the information “close”
to you ...
30

31. Which Trust Metric works better?
■ And under which conditions?
■ Still an open question. [you can work on it ;­) ]
■ Few papers until now evaluate trust metrics:
●
Input data not easily available
●
(advogato.org (8K), FOAF, epinions.com(150K), ... but
not weighted)
■ No papers compare different TMs

Leave­one­out technique
■ Is local better than global? Only for the few users who are
atypical? Computational Expensive? Attack­resistant
(googlebomb)? 31

32. Trust propagation
1
Mena Bill
0.6
0.2
ME 0.8
Doc
■ Trust chains (propagation)
■ Combining different trust chains
■ 0.6 * 1 = 0.6, and 0.8 * 0.2 = 0.16
■ Then average? Not that simple ...
■ And how far does trust propagate? 32

33. Trust metrics open issues
(There are no comparative evaluations of TMs)
■ Cycles are a problem ­­> Order peers based on distance from source user

Trust of users at level k is based only on trust of users at level k­1 (and k)

Trust propagation horizon (computation)
Find all trust paths from source to target
Propagate trust along trust paths
Trust decay: every hop reduces trust (or certainty of trust).
●
A user can't propagate more than received trust.
●
Distrust (trust=0) blocks the propagation.
Trust about quality vs Trust as judger
Tquality(A,C)=f(Tjudger(A,B),Tquality(B,C))
Combine different trust paths
– Unpredictable Trust = minimum trust value.
– There are no globally “bad” users.
1 1
– Warn about Paradoxes or inconsistencies.
1 0 33

34. How to use distrust
■ Distrust? Opinions of distrusted peers should
simply be discarded, otherwise they could
manipulate them to influence our recs
■ Example: suppose we distrust some that is
distrusted by our enemy, then our enemy could
say “I trust A” and we come to distrust A (that
could be anyone ... from pope to bush)
■ But, it is worth knowing about someone who is
trusted by many, even if distrusted by you...
34

35. RS evaluation: how?
■ Back to Recommender Systems:

How do we evaluate RSs performances?

Any ideas?...
35

36. RS evaluation: let us count the ways...
■ Many ways to evaluate Recommender Systems.
■ Leave­one­out: hide one rating and try to predict it

Accuracy: are predictions correct?

Coverage: how often are we able to predict?
■ Accuracy: differences between real value and predicted
value.

MAE, MSE, Weighted MAE, MAUE, ...
■ Ability to identify some new items user will like
(unwatched movie), or bad items (spam, products).
■ Evaluation is still problematic 36

37. Trust in Recommender Systems
How do we exploit trust in RSs?
Instead of computing UserSim of other users, compute
Trust in other users.
Instead of items liked by similar users,
recommend items liked by “trustable” users.
(or combine both methods ...) 37

38. Trust Propagation
ME
6 degrees of separation “theorem” (Stanley Milgram, 1967)
With few trust steps it is possible to reach every person in
the world! (but more steps needed for higher­trust actions)
­­> Ideally, using trust metrics, no more unknown users. 38

39. Trust solves RS problems
■ User Similarity often not computable
➔
trust propagation and “6 degrees” ­> we are
now able to predict trust for many users
■ Cold start
➔
“just add 1
friend”
■ Easy copy­profile Attacks
➔
“you can be similar but if no trust path to
you ...”
■ Hard to understand and control
➔
Showing Trust Networks supports
Explanation 39

40. Epinions.com Experiments
■ Some experiments to show that trust solves RSs problems...
■ Epinions.com users can

Review and rate items (from 1 to 5)

Keep web of trust (trust=1) and block list (trust=0). [Epinions
FAQ says to put in Web of Trust “Reviewers whose reviews and
ratings you have consistently found to be valuable”]
■ Dataset (collected by crawling site):

~50K users, ~140K items, ~660K ratings.

~500K trust statements.
➔
No block list (not shown on site, kept hidden)
40

41. Experimental Setup
Compare performances of CF (1) and trust­aware (2) algorithm
(1) ­ use CF on ratings and compute “similarity” of other users
(2) ­ use Trust Metric and compute “trustworthiness” of other users
Then we can predict ratings based on similar OR trustable users.
Leave­one­out: hide one rating, predict it and compute the error (660.000 ratings!)
41

42. UserSimilarity and Trust
computability
Mean number of Comparable users for Mean number of Comparable users for
All users Cold Start users
Propagating Trust Using Propagating Trust Using
Pearson Pearson
Dist 1 Dist 2 Dist 3 Dist 4 Dist 1 Dist 2 Dist 3 Dist 4
9.88 400 4386 16334 161 2.14 94.54 1675 9121 2.74
42

43. Webs of Trust Grow Quickly...
Mean # Reachable Users (in k steps) for users expressing X trust
statements.
In few steps, you can predict trust in every user! Even for Cold Start
Users!!!
43

44. User Trust Metric
Linear decay based on distance from ME: closer users are more trustable.
Parameter: max propagation distance (mpd) – distance in social network past
which there is no trust.
TrustME(B) = (mpd­distME(B)+1) / mpd
If (distME(B)>mpd) then TrustME(B) = 0
Experiments with mpd=1, 2, 3, 4 called Trust­1, Trust­2, Trust­3, Trust­4
44

45. User Trust Metric
Example: max propagation distance=4
DistME(B) = 1
TME(B) = (4­1+1)/4=1
DistME(B) = 2
TME(B) = 3/4
DistME(B) = 3 ME
TME(B) = 2/4
DistME(B) = 4
TME(B)= 1/4
DistME(B) > 4
TME(B) = 0
45

46. Experimental Results
Rows:
#Expressed Ratings ALL 2 3 4 UserSim = CollaborativeFiltering
User population size 40169 3937 2917 2317
Trust­x = Trust propagation up to distance x
Mean Web of Trust Size 9.88 2.54 3.15 3.64
Ratings UserSim 51% N/A 4% 8%
Coverage Trust-1 28% 10% 11% 12% RatingsCoverage = how many hidden
60% 23% 26% 31%
Trust-2
ratings are predictable.
Trust-3 74% 39% 45% 51%
Trust-4 77% 45% 53% 59%
UsersCoverage = how many users get at
Users UserSim 41% N/A 6% 14% least a prediction
Coverage Trust-1 45% 17% 25% 32% MAE = |real_rating­pred_rating| averaged
56% 32% 43% 53%
Trust-2
over all the ratings.
Trust-3 61% 46% 57% 64%
Trust-4 62% 56% 59% 66% MAUE = |real_rating­pred_rating| averaged
Mean UserSim 0.843 N/A 1.244 1.027 over the ratings of one user, then averaged
Absolute Trust-1 0.837 0.929 0.903 0.840 over all users.
Error Trust-2 0.829 1.050 0.940 0.927
(MAE) Trust-3 0.811 1.046 0.940 0.918
Trust-4 0.805 1.033 0.926 0.903 Columns:
Views over users.
ALL = all the users (with at least 1 rating)
2 = only the subset of users that gave 2
ratings (there are 3937)
(similarly for 3 and 4)...
46

47. Experimental Results
On average, Trust­x
#Expressed Ratings ALL 2 3 4
User population size 40169 3937 2917 2317 achieves better coverage
without loss of accuracy.
Mean Web of Trust Size 9.88 2.54 3.15 3.64
Ratings UserSim 51% N/A 4% 8%
Coverage Trust-1 28% 10% 11% 12%
Trust-2 60% 23% 26% 31%
Trust-3 74% 39% 45% 51%
Trust-4 77% 45% 53% 59%
Users UserSim 41% N/A 6% 14%
Coverage Trust-1 45% 17% 25% 32%
Trust-2 56% 32% 43% 53%
Trust-3 61% 46% 57% 64%
Trust-4 62% 56% 59% 66%
Mean UserSim 0.843 N/A 1.244 1.027
Absolute Trust-1 0.837 0.929 0.903 0.840
Error Trust-2 0.829 1.050 0.940 0.927
(MAE) Trust-3 0.811 1.046 0.940 0.918
Trust-4 0.805 1.033 0.926 0.903
47

48. Experimental Results
On average, Trust­x
#Expressed Ratings ALL 2 3 4
User population size 40169 3937 2917 2317 achieves better coverage
without loss of accuracy.
Mean Web of Trust Size 9.88 2.54 3.15 3.64
Ratings UserSim 51% N/A 4% 8%
Coverage Trust-1 28% 10% 11% 12%
Trust-2 60% 23% 26% 31% UserSim performs well
Trust-3
Trust-4
74%
77%
39%
45%
45%
53%
51%
59%
with heavy raters and
Users UserSim 41% N/A 6% 14% poorly with cold start users.
Coverage Trust-1 45% 17% 25% 32%
Trust-2 56% 32% 43% 53%
Trust-3 61% 46% 57% 64%
Trust-4 62% 56% 59% 66%
Mean UserSim 0.843 N/A 1.244 1.027
Absolute Trust-1 0.837 0.929 0.903 0.840
Error Trust-2 0.829 1.050 0.940 0.927
(MAE) Trust-3 0.811 1.046 0.940 0.918
Trust-4 0.805 1.033 0.926 0.903
48

49. Experimental Results
On average, Trust­x
#Expressed Ratings ALL 2 3 4
User population size 40169 3937 2917 2317 achieves better coverage
without loss of accuracy.
Mean Web of Trust Size 9.88 2.54 3.15 3.64
Ratings UserSim 51% N/A 4% 8%
Coverage Trust-1 28% 10% 11% 12%
Trust-2 60% 23% 26% 31% UserSim performs well
Trust-3
Trust-4
74%
77%
39%
45%
45%
53%
51%
59%
with heavy raters and
Users UserSim 41% N/A 6% 14% poorly with cold start users.
Coverage Trust-1 45% 17% 25% 32%
Trust-2 56% 32% 43% 53%
Trust-3
Trust-4
61%
62%
46%
56%
57%
59%
64%
66%
For cold start users (50% of
Mean UserSim 0.843 N/A 1.244 1.027 the total!), Trust­x achieves
Absolute Trust-1 0.837 0.929 0.903 0.840
Error Trust-2 0.829 1.050 0.940 0.927 also better accuracy.
(MAE) Trust-3
Trust-4
0.811
0.805
1.046
1.033
0.940
0.926
0.918
0.903
For bootstrapping RSs,
asking one trust statement
is better than asking one
rating.
(experiments on 660.000 ratings) 49

50. Centralized vs decentralized
■ Another problem with current RS: centralization
■ Information is Centralized in one server

Your “profile” scattered in many RS (Amazon, B&B, ...)

Profile not reusable (your profile in Amazon is NOT yours)

Recommendation computation out of your control
■ Decentralized

The Web is decentralized: anyone can write whatever she
wants, in whatever “language” she wants (spam is good)

No censorship, innovation can happen on the edges, not a
single mind but many minds... you are in control of what you
produce
50

51. Semantic Web
■ A Web of content designed for and understandable by machines.
(matrix?)
■ Promising Semantic Web formats

FOAF (Friend­Of­A­Friend): trust info <­­­

XFN (Xhtml Friend Network): social info

VoteLink: vote­for, vote­against, vote­abstain links

Blogroll: not semantic!

hReview: review/rating info <­­­

RSS of OutFoxed (http://getoutfoxed.com/rss)

RVW, OpenReview, ...: review/rating info
■ Allow decentralized publishing of information that RSs
aggregate and exploit. 51

52. Note: Adoption of a Language
■ Suppose you can define the language we have to use
for communicating

Which language is better? Chinese? Italiano? The
one you invent?
■ Interesting question, but as long as “good enough”,
matters little (or not at all) for adoption of language.
■ Do you know why keys in your keyboard are placed in
that way? ­­> how standards get adopted ...
■ Who has the power to “propose” changes in the
language of the Web?
52

53. Format adoption
■ Adoption does not depend on quality of the format but
(mainly) on the authority of the proponent.
■ Google can push changes in HTML (example:
rel=nofollow)
■ Certainly Microsoft could (even without you noticing it
or telling it you)
■ I can't.
■ ...But maybe if I create a format that's immediately
useful, it will be taken up by a user community and
spread “virally” (how HTML started)
53

54. FOAF (Friend­Of­A­Friend)
Every peer expresses who she knows (and trusts, with an
extension). Based on RDF (Resource Description Format)
It is already used (some millions of files). Create one FOAF
file and put it in your page! Find more at
http://foaf­project.org
Decentralized publishing advantages: profile not scattered in
many different sites and under user control. A
scutter/crawler can follow “links” (seeAlso) and aggregate
the complete social network.
STOP!!! Downsides to publishing your friendship network?
54

55. FOAF example
<foaf:Person rdf:nodeId=quot;mequot;>
<foaf:name>Paolo Massa</foaf:name>
<foaf:mbox rdf:resource=quot;mailto:massa@itc.itquot; />
...
<foaf:knows rdf:nodeId=quot;friend01quot; />
<trust:trust9 rdf:nodeId=quot;friend01quot; />
</foaf:Person>
<foaf:Person rdf:nodeId=quot;friend001quot;>
<foaf:name>Jennifer Golbeck</foaf:name>
<rdfs:seeAlso rdf:resource=quot;http://cs.umd.edu/~golbeck/daml/golbeckFOAF.rdfquot;/>
</foaf:Person>
Find my FOAF file at http://sra.itc.it/people/massa/paolofoaf.rdf
(and check if you are one of my friends ;­) 55

56. hReview
■ hReview is a simple, open, distributed reviews
format suitable for embedding in (X)HTML, Atom,
RSS, and arbitrary XML.
■ In order to enable and encourage the sharing,
distribution, syndication, and aggregation of
reviews
■ Proposed by Technorati.com on a wiki page
■ http://developers.technorati.com/wiki/hReview
■ You are free to participate and give suggestions
and feedback!
56

57. hReview example
<div class=quot;hreviewquot;>
<span><span class=quot;ratingquot;>5</span> out of 5 stars</span>
<h4 class=quot;summaryquot;><span class=quot;item fnquot;>Crepes on Cole</span> is awesome</h4>
<span>Reviewer: <span class=quot;reviewer fnquot;>Tantek</span> ­
<abbr class=quot;dtreviewquot; title=quot;20050418T2300­0700quot;>April 18, 2005</abbr></span>
<blockquote class=quot;descriptionquot;><p>Crepes on Cole is one of the best little creperies in
San Francisco. Excellent food and service. Plenty of tables in a variety of sizes for
parties large and small. Window seating makes for excellent people watching to/from
the N­Judah which stops right outside. I've had many fun social gatherings here, as
well as gotten plenty of work done thanks to neighborhood WiFi. </p></blockquote>
<p>Visit date: <span>April 2005</span></p>
<p>Food eaten: <span>Florentine crepe</span></p>
</div>
57

58. Conclusions
■ Info Filtering:
■ From Content­based ... to Collaborative Filtering ... to
Trust­aware (?)
■ Trust is a simple and complicated concept
■ It is a rapidly evolving and increasingly important topic:
there is room for your contributions!
■ Forecast: In 3 years, anyone will publish her opinions
(about stuff, organizations...people, ideas?) in some
semantic format and trust­aware aggregators
(googletrust?) will help in coping with an increased
information overload.
58

59. Licence of these slides
These slides are released under
Creative Commons
Attribution­ShareAlike 2.5
You are free:
* to copy, distribute, display, and perform the work
* to make derivative works
* to make commercial use of the work
Under the following conditions:
Attribution. You must attribute the work in the manner specified by the author or licensor.
Share Alike. If you alter, transform, or build upon this work, you may distribute the resulting work only under a license
identical to this one.
* For any reuse or distribution, you must make clear to others the license terms of this work.
* Any of these conditions can be waived if you get permission from the copyright holder.
Your fair use and other rights are in no way affected by the above.
More info at http://creativecommons.org/licenses/by­sa/2.5/
59

60. The END!
THE END!
Thanks for your attention.
Questions?
60