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Reasoning about Weighted Semantic User Profiles through Collective Confidence Analysis: A Fuzzy Evaluation
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), Communication: Services and Infrastucture, Software and Computer Systems, SCS.ORCID iD: 0000-0002-4722-0823
2010 (English)In: ADVANCES IN INTELLIGENT WEB MASTERING-2, PROCEEDINGS    / [ed] Snasel V; Szczepaniak PS; Abraham A; Kacprzyk J, 2010, Vol. 67, 71-81 p.Conference paper, Published paper (Refereed)
Abstract [en]

User profiles are vastly utilized to alleviate the increasing problem of so called information overload. Many important issues of Semantic Web like trust, privacy, matching and ranking have a certain degree of vagueness and involve truth degrees that one requires to present and reason about. In this ground, profiles tend to be useful and allow incorporation of these uncertain attributes in the form of weights into profiled materials. In order to interpret and reason about these uncertain values, we have constructed a fuzzy confidence model, through which these values could be collectively analyzed and interpreted as collective experience confidence of users. We analyze this model within a scenario, comprising weighted user profiles of a semantically enabled cultural heritage knowledge platform. Initial simulation results have shown the benefits of our mechanism for alleviating problem of sparse and empty profiles.

Place, publisher, year, edition, pages
2010. Vol. 67, 71-81 p.
Series
Advances in Intelligent and Soft Computing, ISSN 1867-5662 ; 67
Keyword [en]
Confidence, Fuzzy Inference, Semantic User Profiles, Personalization, Reasoning, Uncertainty Evaluation
National Category
Computer and Information Science
Identifiers
URN: urn:nbn:se:kth:diva-29226DOI: 10.1007/978-3-642-10687-3_7ISI: 000281727500007Scopus ID: 2-s2.0-84886024779OAI: oai:DiVA.org:kth-29226DiVA: diva2:392960
Conference
6th Atlantic Web Intelligence Conference, Charles Univ, Fac Math & Phys, Prague, CZECH REPUBLIC, SEP, 2009
Note
QC 20110128Available from: 2011-01-28 Created: 2011-01-27 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.
Series
TRITA-ICT-ECS AVH, ISSN 1653-6363 ; 13:10
Keyword
trust, userprofiling, userprofiles, privacy, interest, socialnetwork, recommendersystems
National Category
Information Systems
Identifiers
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)
Opponent
Supervisors
Note

QC 20130219

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

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Matskin, Mihhail

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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
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  • de-DE
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  • Other locale
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Output format
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