Trust in Recommender Systems a historical overview and recent developments

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

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

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

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

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. ■ \"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.\" Juliet Schor 5

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

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.) ■ \"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!\" (Siegel, B.L. (1984, April 15). Knowledge with commitment: Teaching is the central task of the university. Vital Speeches of the Day, 50, 394.) ■ \"In the last 30 years mankind has produced more information than in the previous 5,000.\" (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. ■ \"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].) ■ \"The average Fortune 1000 worker already is sending and receiving approximately 178 messages and documents each day, according to a recent study, \"Managing Corporate Communications in the Information Age.\" (Boles, M. (1997) Help! Information overload. Workforce, 76, 20.) ■ \"Dr Dharma Singh Khalsa, in his book Brain Longevity,...says the average American sees 16,000 advertisements, logos, and labels in a day.\" (Gore, A. (1998, January 18) . Stressed? Maybe it's information overload. Sun Herald, 27.) ■ University of California Berkely has a \"How Much Information\" project which studies the amount of information produced each year. \"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.\" 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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

FOAF example <foaf:Person rdf:nodeId=\"me\"> <foaf:name>Paolo Massa</foaf:name> <foaf:mbox rdf:resource=\"mailto:massa@itc.it\" /> ... <foaf:knows rdf:nodeId=\"friend01\" /> <trust:trust9 rdf:nodeId=\"friend01\" /> </foaf:Person> <foaf:Person rdf:nodeId=\"friend001\"> <foaf:name>Jennifer Golbeck</foaf:name> <rdfs:seeAlso rdf:resource=\"http://cs.umd.edu/~golbeck/daml/golbeckFOAF.rdf\"/> </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

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

hReview example <div class=\"hreview\"> 5 out of 5 stars <h4 class=\"summary\">Crepes on Cole is awesome</h4> Reviewer: Tantek <abbr class=\"dtreview\" title=\"20050418T23000700\">April 18, 2005</abbr> <blockquote class=\"description\">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. </blockquote> Visit date: April 2005 Food eaten: Florentine crepe </div> 57

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

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

Trust in Recommender Systems a historical overview and recent developments

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