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<article lang="en"><title>Analysis, Design and Implementation of Personalized Recommendation Algorithms Supporting Self-organized Communities</title><articleinfo><subtitle>Fortschr.-Ber. VDI Reihe 22 Nr. 23.</subtitle><authorblurb><para role="Verfasser">Author: Yang, Fan<?d-linebreak?><ulink url="mailto:Fan.Yang@FernUni-Hagen.de"><phrase role="Hyperlink">Fan.Yang@FernUni-Hagen.de</phrase></ulink> </para></authorblurb><authorblurb><para role="Institut">FernUniversität in Hagen<?d-linebreak?>Department of Mathematics and Computer Science<?d-linebreak?>Chair of Data Processing Technologie<?d-linebreak?>Universitätsstraße 27<?d-linebreak?>58097 Hagen</para></authorblurb><authorblurb><para role="Verfasser">Supervisors:</para></authorblurb><authorblurb><para role="Verfasser">Prof. Dr.-Ing. Bernd Krämer<?d-linebreak?><ulink url="mailto:lehrgebiet.dvt@fernuni-hagen.de"><phrase role="Hyperlink">lehrgebiet.dvt@fernuni-hagen.de</phrase></ulink> </para></authorblurb><authorblurb><para role="Institut">FernUniversität in Hagen<?d-linebreak?>Department of Mathematics and Computer Science<?d-linebreak?>Chair of Data Processing Technologie<?d-linebreak?>Universitätsstraße 27<?d-linebreak?>58097 Hagen</para></authorblurb><authorblurb><para role="Verfasser">Prof. Ruimin Shen<?d-linebreak?><ulink url="mailto:rmshen@mail.sjtu.edu.cn"><phrase role="Hyperlink">rmshen@mail.sjtu.edu.cn</phrase></ulink> </para></authorblurb><authorblurb><para role="Institut">Deptartment of Computer Science and Engineering<?d-linebreak?>Shanghai Jiao Tong University<?d-linebreak?>P.R. China</para></authorblurb><authorblurb><para role="Institut" /></authorblurb><authorgroup><author><firstname>Fan</firstname><surname>Yang</surname><affiliation><orgname>FernUniversität in Hagen, Department of Mathematics and Computer Science, Chair of Data Processing Technologie</orgname></affiliation></author></authorgroup><biblioid class="uri">urn:nbn:de:0009-5-20004</biblioid><keywordset><keyword>information retrieval</keyword><keyword>personalized recommendation</keyword><keyword>self-organized community</keyword><keyword>e-learning</keyword><keyword>collaborative filtering</keyword></keywordset><subjectset scheme="pacs"><subject>software techniques and systems</subject><subject>computer communications software</subject><subject>knowledge engineering techniques</subject><subject>knowledge engineering tools</subject></subjectset><subjectset scheme="ddc"><subject>data processing computer science</subject><subject>data processing computer science – dictionaries, encyclopedias, concordances</subject><subject>computer programming, programs, data</subject><subject>special computer methods</subject><subject>learning</subject><subject>electronic distance education</subject></subjectset><legalnotice><title>Licence</title><para>Any party may pass on this Work by electronic means and make it available for download under the terms and conditions of the free Digital Peer Publishing Licence. The text of the licence may be accessed and retrieved via Internet at http://www.dipp.nrw.de/lizenzen/dppl/fdppl/f-DPPL_v1_de_11-2004.html</para></legalnotice><volumenum>5</volumenum><issuenum>1</issuenum><biblioset relation="journal"><issn>1860-7470</issn><title>e-learning and education</title></biblioset></articleinfo><section><title /><para>Hochschulschrift (Dissertation)</para><para>VDI-Verlag, Düsseldorf, 2006</para><para>ISBN: 3-18-302322-2<?d-linebreak?>ISSN: 1439-958X</para><para>FernUniversität in Hagen, Chair of Data Processing Technologie; Jiaotong-Universität Shanghai </para><para>URL: <ulink url="http://deposit.fernuni-hagen.de/2195/1/fy-thesis-with-CV.pdf"><phrase role="Hyperlink">http://deposit.fernuni-hagen.de/2195/1/fy-thesis-with-CV.pdf</phrase></ulink> </para><para>In order to tackle the “information overload” problem, researchers began to investigate various automated information filtering (IR) techniques that aim to select those information fragments out of large volumes of (dynamically generated) information that are most likely to meet the user’s information requirements. Since then, various methods ranging from content-based filtering (CBF) to collaborative filtering (CF), data mining (DM), and artificial intelligence (AI) have been developed. However, many web applications including e-learning, which lies in the focus of this thesis, exhibit inherent properties such as openness and distribution that are not addressed by existing solutions. They were designed with a centralized architecture in mind and do not scale well. In addition, learning behavior is a very complicated process that requires a more elaborate scheme than exists today to capture relevant user features for the purpose of matching learner interests. To accommodate these needs, this thesis proposes a novel personalized recommendation model and provides an operational framework with a high degree of generality and scalability through research on:</para><itemizedlist mark="disc" spacing="normal"><listitem><para role="List Bullet">modeling and analysis of dynamic user behavior in open environments,</para></listitem><listitem><para role="List Bullet">discovery of users with similar interests in distributed communities, and</para></listitem><listitem><para role="List Bullet">self-organized bi-directional community construction.</para></listitem></itemizedlist><para>The theoretical results produced in this research have been applied to the real e-learning environment at Shanghai Jiao Tong University to evaluate their effectiveness in monitoring learning communities and recommending personalized resources in large-scale network education.</para></section></article>
