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An Adaptive Framework for Discovery andMining of User Profiles from Social Web-based Interest Communities
KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.ORCID iD: 0000-0002-4722-0823
2013 (English)In: The Influence of Technology on Social Network Analysis and Mining / [ed] Özyer, T.; Rokne, J.; Wagner, G.; Reuser, A., Wien: Springer, 2013, Vol. 23, 497-519 p.Chapter in book (Refereed)
Abstract [en]

Abstract Within this paper we introduce an adaptive framework for semi- tofully-automatic discovery, acquisition and mining of topic style interest profilesfrom openly accessible social web communities. To do such, we build an adaptivetaxonomy search tree from target domain (domain towards which we are gatheringand processing profiles for), starting with generic concepts at root moving down tospecific-level instances at leaves, then we utilize one of proposed Quest schemesto read the concept labels from the tree and crawl the source social networkrepositories for profiles containing matching and related topics. Using machinelearning techniques, cached profiles are then mined in two consecutive steps,utilizing a clusterer and a classifier in order to assign and predict correct profilesto their corresponding clustered corpus, which are retrieved later on by an ontology-based recommender to suggest and recommend the community members with theitems of their similar interest. Focusing on increasingly important digital culturalheritage context, using a set of profiles acquired from an openly accessible socialnetwork, we test the accuracy and adaptivity of framework. We will show that a tradeoff between schemes proposed can lead to adaptive discovery of highly relevant profiles.

Place, publisher, year, edition, pages
Wien: Springer, 2013. Vol. 23, 497-519 p.
Series
Lecture Notes in Social Networks, 23
Keyword [en]
interest profile, profile mining, semantic recommender, social web mining, community extraction, social network
National Category
Information Systems
Identifiers
URN: urn:nbn:se:kth:diva-118487DOI: 10.1007/978-3-7091-1346-2_22ISBN: 978-3-7091-1345-5 (print)OAI: oai:DiVA.org:kth-118487DiVA: diva2:606491
Note

QC 20130219

Available from: 2013-02-19 Created: 2013-02-19 Last updated: 2016-12-15Bibliographically 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

Open Access in DiVA

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Publisher's full texthttp://www.springer.com/computer/book/978-3-7091-1345-5

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Matskin, Mihhail

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Citation style
  • apa
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