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Analysis, Design and Implementation of Personalized Recommendation Algorithms Supporting Self-organized Communities

Fortschr.-Ber. VDI Reihe 22 Nr. 23.

Author: Yang, Fan
Fan.Yang@FernUni-Hagen.de

FernUniversität in Hagen
Department of Mathematics and Computer Science
Chair of Data Processing Technologie
Universitätsstraße 27
58097 Hagen

Supervisors:

Prof. Dr.-Ing. Bernd Krämer
lehrgebiet.dvt@fernuni-hagen.de

FernUniversität in Hagen
Department of Mathematics and Computer Science
Chair of Data Processing Technologie
Universitätsstraße 27
58097 Hagen

Prof. Ruimin Shen
rmshen@mail.sjtu.edu.cn

Deptartment of Computer Science and Engineering
Shanghai Jiao Tong University
P.R. China

urn:nbn:de:0009-5-20004

Hochschulschrift (Dissertation)

VDI-Verlag, Düsseldorf, 2006

ISBN: 3-18-302322-2
ISSN: 1439-958X

FernUniversität in Hagen, Chair of Data Processing Technologie; Jiaotong-Universität Shanghai

URL: http://deposit.fernuni-hagen.de/2195/1/fy-thesis-with-CV.pdf

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:

  • modeling and analysis of dynamic user behavior in open environments,

  • discovery of users with similar interests in distributed communities, and

  • self-organized bi-directional community construction.

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.

License

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 License. The text of the license may be accessed and retrieved via Internet at http://www.dipp.nrw.de/lizenzen/dppl/fdppl/f-DPPL_v1_de_11-2004.html

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