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Invited talk at Future Networked Technologies / FIT-IT research calls opening Graz, Austria - June 9th, 2010
 

Invited talk at Future Networked Technologies / FIT-IT research calls opening Graz, Austria - June 9th, 2010

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    Invited talk at Future Networked Technologies / FIT-IT research calls opening Graz, Austria - June 9th, 2010 Invited talk at Future Networked Technologies / FIT-IT research calls opening Graz, Austria - June 9th, 2010 Presentation Transcript

    • 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)
    • What I talk about My personal visions and suggestions for collaborative research projects funded by FIT-IT on the 3 topics of the calls
      • Trust in IT Systems
      • Semantic Systems and Services
      • 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 -
    • Who am I
        PhD in “Trust Metrics in Decentralized Recommender Systems” from University of Trento (2006) Since April 2008, Head of SoNet research group at Bruno Kessler Foundation (FBK), Trento, Italy
        • Focus is “Social Networking”
        • 6 people
    • Bruno Kessler Foundation
      • ~400 researchers (IT,Microelectronics,...)
      • Funded by Autonomous Province of Trento, North-East Italy www.fbk.eu
      • Not only SoNet group! ;)
        • Data and Knowledge Management http://dkm.fbk.eu (Semantic Systems)
        • Web of Data http://wed.fbk.eu (Semantic Services)
        • Interactive interfaces http://i3.fbk.eu (Visual Computing)
        • Predictive Models for Biomedicine & Environment http://mpba.fbk.eu (Visual Computing)
        • Security and Trust http://st.fbk.eu/ (Trust in IT)
    • OUTLINE
      • Research on Trust in Recommender Systems as motivating example
      • Research on Social Networking at SoNet
      • Promising directions for Trust in IT Systems and Semantic Systems and Services
    • 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”)
    • 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".
    • 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
    • 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
    • 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
    • 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
    • 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
    • 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 (eBay, Slashdot, ). Work bad for controversial people (Bush)
      • Local Trust Metrics
        • Trust is subjective --> consider personal views ( trust “Bill”? )
        • 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
    • Global vs Local Trust Metrics
      • Global vs Local Trust Metrics
        • Objective vs Subjective
        • Neutral Point Of View vs Your personal POV
        • Tyranny of the majority vs daily me/echo chambers
      • “Trust in IT”: TMs 4 Hard vs Soft Security?
        • Soft security [2]: “protects something from harm in quiet and unobtrusive ways, often invisibly and after the fact, rather than with visible barriers before the fact”.
        • 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
    • Trust in Recommender Systems
      • How Trust Metrics are used in Recommender Systems (ex: Amazon)?
      • Recommend items (ex: books) appreciated by users you may trust (according to the chosen Trust Metric),
      • instead of recommending items appreciated by similar users (Collaborative Filtering)
    • Massa PhD Thesis Conclusions Experiments on real data crawled from Epinions.com
      • ~132K users, ~14M items ratings, ~841K trust statements (positive and negative)
      • Trust-aware RSs smaller errors, higher coverage, especially for cold start users [3]
      • 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).
    • Generalizing suggestions
      • Key points of this research on Trust-aware Recommender Systems:
        • Open Web
        • Real data from real users
    • Share the data!
      • I released all the collected datasets at http://www.trustlet.org
        • Epinions.com, Advogato.org, …
      • Also good for improving visibility of your research!
      • Nowadays data are starting to become available, mainly because of electronic trails we leave on Social Network Sites
    • OUTLINE
      • Research on Trust in Recommender Systems as motivating example
      • Research on Social Networking at SoNet
      • Promising directions for “Trust in IT Systems” and “Semantic Systems and Services”
    • Social Networking Social Network Sites [5]:
      • Facebook, Twitter, …
      SoNet group at FBK focuses on this … [5] boyd, d. m., & Ellison, N. B. (2007). Social network sites: Definition, history, and scholarship. Journal of Computer-Mediated Communication, 13(1), article 11.
    • 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!
    • User talk page http://en.wikipedia.org/wiki/User_talk:Phauly
    • User talk page http://en.wikipedia.org/wiki/User_talk:Phauly 0.6
    • User talk page http://en.wikipedia.org/wiki/User_talk:Phauly 0.6 Shell 1 Phauly
    • User talk page http://en.wikipedia.org/wiki/User_talk:Phauly 0.6 Shell 1 Phauly
    • User talk page http://en.wikipedia.org/wiki/User_talk:Phauly 0.6 Shell 1 Phauly Martin
    • User talk page http://en.wikipedia.org/wiki/User_talk:Phauly 0.6 Public conversations! Real data! Shell 1 Phauly Martin
    • Directed and Weighted social network of who “talks” to whom on Wikipedia ME Mena 9 Doc 96 1 Cory Ben 6 Mary 16 2
    • Vec.wikipedia.org: just 922 nodes! En.wikipedia.org social network has millions of nodes!
    • SoNet research lines on Wikipedia
      • Structural properties of different Wikipedia social networks (SNA)
      • How are talk pages used by different group of users: content analysis on Venetian Wikipedia
      • Comparing different ways of extracting Wikipedia social network
      • (future) Longitudinal: evolution in time of a Wikipedia social network
      • Why talk about Wikipedia SN?
      • Interesting! ;)
      • Neutral Point Of View (NPOV) -> tyranny of the majority?
        • But we see edit wars, personal POVs, conflicts of interests
        • Trust metrics for personalizing content on wikipedia?
      • Soft security is how permissions are managed on Wikipedia: lessons for “Trust in IT”?
      • In few slides, I talk about “Semantic services” thanks to Wikipedia: DBPedia
      • Why did I talk about Wikipedia social network research?
      • It's another example of research:
        • Open web
        • Real data from real people
    • SoNet research on Enterprise2.0
      • We explored Enterprise2.0, created an open source platform Taolin
        • http://taolin.fbk.eu
      • And did research on its usage in FBK and relationships with social capital [6]
      • BUT we dropped this line
        • NO Open web: Intranet!
        • 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.
    • By the way, I assume Wikipedia as the prototype of the organization of the future
        • Horizontal
        • Ad-hoc, rules/policies are negotiated continuously
        • Soft security, instead of Hard security [2]
          • Messier but is not reality a mess?
      [2] L. Rasmusson, S. Jansson. Simulated Social Control for Secure Internet Commerce. New Security Paradigms Workshop. ACM, 1996
    • OUTLINE
      • Research on Trust in Recommender Systems as motivating example
      • Research on Social Networking at SoNet
      • Promising directions for “Trust in IT Systems” and “Semantic Systems and Services”
      • Going back to open web and real data ,
        • Don't invent some new ways of thinking/speaking and expect people to adopt it!
          • “I propose to change all the web into this new format, then we will be able to do lots of things ...”
          • “If all the organizations would use this tool, then ...”
        • Look for what is already available on the Web, build on it, look at it with different eyes.
      • Going back to open web and real data ,
        • Don't invent some new ways of thinking/speaking and expect people to adopt it!
          • “I propose to change all the web into this new format, then we will be able to do lots of things ...”
          • “If all the organizations would use this tool, then ...”
        • Look for what is already available on the Web, build on it, look at it with different eyes.
      • Ex: billions of links were already there when Google founders “saw” them and used them for improving search engines!
        • PageRank as 1 of 3 examples in the original definition of Web2.0 by Tim O'Reilly (2004)
    • Other examples of this (relevant for “Semantic Systems and Services”)
      • DBPedia.org
        • Instead of asking people to “express knowledge in RDF”, automatically extract knowledge (in form of RDF triples) from Wikipedia!
        • Then SPARQL it (ex: “People who were born in Berlin before 1900”)
      • Another example of research:
        • Open Web (from Web, to Web: mashable!)
        • Real data from real users (info was there!)
    • More examples Other examples of this (relevant for “Semantic Systems and Services”)
      • Microformats / RDFa
        • Both looked into what is there (Web) and propose small/achievable changes
      • Another example of research:
        • Open web
        • Real data from real people
    • 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>
    • Linked Data Other examples of this (relevant for “Semantic Systems and Services”)
      • LinkedData.org
      Instead of asking people to “express knowledge in RDF”, transform available datasets! And link them!
        • Link your research and data to what is already existent (The essence of the Web is the link!)
      May 2009: 4.2 billion RDF triples, interlinked by around 142 million RDF links.
    •  
    • Conclusions - Suggestions
      • Lots of data are there and coming
        • SocialNetworkSites have APIs for exporting data
        • data.gov, data.gov.uk and similar initiatives worldwide
      • Don't make unrealistic assumptions
      • Look for what is available (Web)
        • Possibly with different glasses
      • Propose projects for improving the open Web, and used by real users:
        • Wrt “Trust in IT”, “Semantic Systems and Services”
    • Interested in collaborating with SoNet or other FBK groups? Get in touch! Now or at http://sonet.fbk.eu Thank you! Questions?
    • Licence of these slides These slides are released under Creative Commons Attribution-ShareAlike 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/by-sa/2.5/