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Forging Trust and Privacy with User Modeling Frameworks: An Ontological Analysis
(Department of Computer Science, Universita' degli Studi di Torino)
KTH, School of Information and Communication Technology (ICT), Communication: Services and Infrastucture, Software and Computer Systems, SCS.
KTH, School of Information and Communication Technology (ICT), Electronic, Computer and Software Systems, ECS.ORCID iD: 0000-0002-4722-0823
2011 (English)In: The First International Conference on Social Eco-Informatics: (SOTICS 2011) / [ed] Dokoohaki and Hall, IARIA , 2011, 43-48 p.Conference paper (Refereed)
Abstract [en]

With the ever increasing importance of social net- working sites and services, socially intelligent agents who are responsible for gathering, managing and maintaining knowledge surrounding individual users are of increasing interest to both computing research communities as well as industries. For these agents to be able to fully capture and manage the knowledge about a user’s interaction with these social sites and services, a social user model needs to be introduced. A social user model is defined as a generic user model (model capable of capturing generic information related to a user), plus social dimensions of users (models capturing social aspects of user such as activities and social contexts). While existing models capture a proportion of such information, they fail to model and present ones of the most important dimensions of social connectivity: trust and privacy. To this end, in this paper, we introduce an ontological model of social user, composed by a generic user model component, which imports existing well-known user model structures, a social model, which contains social dimensions, and trust, reputation and privacy become the pivotal concepts gluing the whole ontological knowledge models together.

Place, publisher, year, edition, pages
IARIA , 2011. 43-48 p.
Keyword [en]
ontologies, privacy, semantic adaptive social web, trust and reputation, user modeling
National Category
Computer and Information Science
URN: urn:nbn:se:kth:diva-84839ISBN: 978-1-61208-163-2OAI: diva2:499905
The First International Conference on Social Eco-Informatics

QC 20120215

Available from: 2012-02-15 Created: 2012-02-13 Last updated: 2013-02-19Bibliographically approved
In thesis
1. Trust-Based User Profiling
Open this publication in new window or tab >>Trust-Based User Profiling
2013 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

We have introduced the notion of user profiling with trust, as a solution to theproblem of uncertainty and unmanageable exposure of personal data duringaccess, retrieval and consumption by web applications. Our solution sug-gests explicit modeling of trust and embedding trust metrics and mechanismswithin very fabric of user profiles. This has in turn allowed information sys-tems to consume and understand this extra knowledge in order to improveinteraction and collaboration among individuals and system. When formaliz-ing such profiles, another challenge is to realize increasingly important notionof privacy preferences of users. Thus, the profiles are designed in a way toincorporate preferences of users allowing target systems to understand pri-vacy concerns of users during their interaction. A majority of contributionsof this work had impact on profiling and recommendation in digital librariescontext, and was implemented in the framework of EU FP7 Smartmuseumproject. Highlighted results start from modeling of adaptive user profilesincorporating users taste, trust and privacy preferences. This in turn led toproposal of several ontologies for user and content characteristics modeling forimproving indexing and retrieval of user content and profiles across the plat-form. Sparsity and uncertainty of profiles were studied through frameworksof data mining and machine learning of profile data taken from on-line so-cial networks. Results of mining and population of data from social networksalong with profile data increased the accuracy of intelligent suggestions madeby system to improving navigation of users in on-line and off-line museum in-terfaces. We also introduced several trust-based recommendation techniquesand frameworks capable of mining implicit and explicit trust across ratingsnetworks taken from social and opinion web. Resulting recommendation al-gorithms have shown to increase accuracy of profiles, through incorporationof knowledge of items and users and diffusing them along the trust networks.At the same time focusing on automated distributed management of profiles,we showed that coverage of system can be increased effectively, surpassingcomparable state of art techniques. We have clearly shown that trust clearlyelevates accuracy of suggestions predicted by system. To assure overall pri-vacy of such value-laden systems, privacy was given a direct focus when archi-tectures and metrics were proposed and shown that a joint optimal setting foraccuracy and perturbation techniques can maintain accurate output. Finally,focusing on hybrid models of web data and recommendations motivated usto study impact of trust in the context of topic-driven recommendation insocial and opinion media, which in turn helped us to show that leveragingcontent-driven and tie-strength networks can improve systems accuracy forseveral important web computing tasks.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2013. xi, 48 p.
TRITA-ICT-ECS AVH, ISSN 1653-6363 ; 13:10
trust, userprofiling, userprofiles, privacy, interest, socialnetwork, recommendersystems
National Category
Information Systems
urn:nbn:se:kth:diva-118488 (URN)978-91-7501-651-1 (ISBN)
Public defence
2013-03-08, C1 Sal, Electrum, ICT/KTH, Isafjordsgatan 20, Kista, 13:00 (English)

QC 20130219

Available from: 2013-02-19 Created: 2013-02-19 Last updated: 2014-01-24Bibliographically approved

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