1. Trust in Recommender Systems and Social Networks Paolo Massa SoNet @ FBK, Trento, Italy http://sonet.fbk.eu Invited talk at Future Networked Technologies / FIT-IT research calls opening Graz, Austria - June 9th, 2010 Slides licenced under CreativeCommons Attribution-ShareAlike (see last slide for more info)

3. Semantic Systems and Services

4. Visual Computing … starting from what I research(ed) on There will be people submitting projects, people evaluating projects: I want to tell you what I think would be more beneficial as research (and what less). At least give you food for thought … – ignite discussion -

6. 6 people

8. Funded by Autonomous Province of Trento, North-East Italy www.fbk.eu

10. Web of Data http://wed.fbk.eu (Semantic Services)

11. Interactive interfaces http://i3.fbk.eu (Visual Computing)

12. Predictive Models for Biomedicine & Environment http://mpba.fbk.eu (Visual Computing)

13. Security and Trust http://st.fbk.eu/ (Trust in IT)

15. Research on Social Networking at SoNet

16. Promising directions for Trust in IT Systems and Semantic Systems and Services

17. Trust My definition of “Trust”: explicit rating of a user on another user about the perceived quality of the user's characteristics Ex: “I trust Mena as 0.9” “ Trust” as social relationship and NOT about security (later I touch “soft security”)

18. Trust usage for real Explicit trust statement “I trust Mena as 0.9” (in [0,1]) E-marketplaces: Ebay.com , Epinions.com, Amazon.com Social Network Sites: Facebook, Twitter, Flickr , ... Job sites: LinkedIn, Ryze, … News sites: Slashdot.org, Kuro5hin.org P2P networks: eDonkey, Gnutella, JXTA Open Source Developers communities: Advogato, Affero, Github Hospitalityclub, couchsurfing: host in your house unknown people! Network of personal weblogs (blogroll) Semantic Web: FOAF (Friend-Of-A-Friend), XFN Google (and Yahoo!): TrustRank, PageRank [1] P. Massa (2006). A survey of trust use in current real systems. In "Trust in E-services: Technologies, Practices and Challenges".

19. Trust Statement Explicit trust statement “I trust Mena as 0.9” (in [0,1]) A node is a user. A direct edge is a trust statement ME Mena 0.9

20. Trust Network - weighted ( 0=distrust, 1=max trust) - asymmetric - subjective A node is a user. Properties of Trust: A direct edge is a trust statement Aggregate all trust statements -> Trust Network (a social network with trust as social relationship) ME Mena 0.9 Doc 1 0 Cory Ben 0.6 Mary 1 0.2

21. Trust Network What can you do with a Trust network? Social Network Analysis (SNA): Computing centrality, indegrees, Powerlaws for unveiling the Topology of the network What I did? Focusing on trust metrics ME Mena 0.9 Doc 1 0 Cory Ben 0.6 Mary 1 0.2

22. Trust Metrics ? ? Trust Metric: Uses existing edges for predicting values of trust for non-existing edges. Thanks to trust propagation, if you trust someone, then you have some degree of trust in anyone that person trusts. ME Mena 0.9 Doc 1 Cory 0.2 Ben 0.6 Mary 1 0

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

26. Local can be more effective if people are not standardized. How much can Bill be trusted? On average (by the community)? By Mary? And by ME? People A trusts can be totally different from people trusted by B PageRank is Global. For web pages it works enough well but problems with High controversial topics abort or jew I tend to use Reputation for this “average” trust Trust for the personal prediction. Bill is Bill Gates Global trust metrics tend to standardize opinions ME Mena 1 Doc 1 Mary Bill 1 0

28. Neutral Point Of View vs Your personal POV

30. Ex: Ad-hoc, fluid, on-the-fly file permissions [2] L. Rasmusson, S. Jansson. Simulated Social Control for Secure Internet Commerce. New Security Paradigms Workshop. ACM, 1996

32. instead of recommending items appreciated by similar users (Collaborative Filtering)

34. Trust-aware RSs smaller errors, higher coverage, especially for cold start users [3]

35. Local TMs very effective, especially on controversial people (>20%!) [4] [3] P. Massa, P. Avesani (2007). Trust-aware Recommender Systems. ACM Recommender Systems Conference, Minneapolis, Minnesota, USA. [4] P. Massa, P. Avesani (2007). Trust metrics on controversial users: balancing between tyranny of the majority and echo chambers. International Journal on Semantic Web and Information Systems (IJSWIS).

37. Real data from real users

40. Research on Social Networking at SoNet

41. Promising directions for “Trust in IT Systems” and “Semantic Systems and Services”

43. Wikipedia as a social network The peak of User generated content! SoNet focus is on Wikipedia as SN Millions of users!!! We work on the social part of Wikipedia, not the content part!

44. User talk page http://en.wikipedia.org/wiki/User_talk:Phauly

45. User talk page http://en.wikipedia.org/wiki/User_talk:Phauly 0.6

46. User talk page http://en.wikipedia.org/wiki/User_talk:Phauly 0.6 Shell 1 Phauly

47. User talk page http://en.wikipedia.org/wiki/User_talk:Phauly 0.6 Shell 1 Phauly

48. User talk page http://en.wikipedia.org/wiki/User_talk:Phauly 0.6 Shell 1 Phauly Martin

49. User talk page http://en.wikipedia.org/wiki/User_talk:Phauly 0.6 Public conversations! Real data! Shell 1 Phauly Martin

50. Directed and Weighted social network of who “talks” to whom on Wikipedia ME Mena 9 Doc 96 1 Cory Ben 6 Mary 16 2

51. Vec.wikipedia.org: just 922 nodes! En.wikipedia.org social network has millions of nodes!

53. How are talk pages used by different group of users: content analysis on Venetian Wikipedia

54. Comparing different ways of extracting Wikipedia social network

55. (future) Longitudinal: evolution in time of a Wikipedia social network

59. In few slides, I talk about “Semantic services” thanks to Wikipedia: DBPedia

62. Real data from real people

65. NO Real data from real people: few users! [6] An Empirical Analysis on Social Capital and Enterprise 2.0 Participation in a Research Institute (2010). Asonam, Conf. on Advances in Social Networks Analysis and Mining.

67. Ad-hoc, rules/policies are negotiated continuously

70. Research on Social Networking at SoNet

71. Promising directions for “Trust in IT Systems” and “Semantic Systems and Services”

78. Real data from real users (info was there!)

80. Real data from real people

81. hReview example Microformat for reviews of items (and ratings): for Trust-aware Rss! <div class=&quot;hreview&quot;> <span><span class=&quot; rating &quot;>5</span> out of 5 stars</span> <h4 class=&quot;summary&quot;><span class=&quot;item fn&quot;>Crepes on Cole</span> is awesome</h4> <span>Reviewer: <span class=&quot;reviewer fn&quot;>Tantek</span> - <abbr class=&quot;dtreview&quot; title=&quot;20050418T2300-0700&quot;>April 18, 2005</abbr></span> <blockquote class=&quot;description&quot;><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 N-Judah 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>

87. Interested in collaborating with SoNet or other FBK groups? Get in touch! Now or at http://sonet.fbk.eu Thank you! Questions?

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