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Intelligent techniques for recommender systems

Ren, Yongli 2013, Intelligent techniques for recommender systems, Ph.D thesis, School of Information Technology, Deakin University.

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Title Intelligent techniques for recommender systems
Author Ren, Yongli
Institution Deakin University
School School of Information Technology
Faculty Faculty of Science, Engineering and Built Environment
Degree type Research doctorate
Degree name Ph.D
Thesis advisor Zhou, WanleiORCID iD for Zhou, Wanlei orcid.org/0000-0002-1680-2521
Li, GangORCID iD for Li, Gang orcid.org/0000-0003-1583-641X
Date submitted 2013-07
Keyword(s) Recommender systems
Collaborative filtering
Imputation techniques
Low-rank subspace techniques
Optimizations techniques
Rating pattern subspace
Summary This thesis focuses on the data sparsity issue and the temporal dynamic issue in the context of collaborative filtering, and addresses them with imputation techniques, low-rank subspace techniques and optimizations techniques from the machine learning perspective. A comprehensive survey on the development of collaborative filtering techniques is also included.
Language eng
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 890205
Description of original xiv, 171 pages : tables, graphs, some coloured
Copyright notice ┬ęThe Author. All Rights Reserved
Free to Read? Yes
Persistent URL http://hdl.handle.net/10536/DRO/DU:30062528

Document type: Thesis
Collections: Higher degree theses (full text)
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Created: Thu, 17 Apr 2014, 15:12:11 EST by Belinda Lee

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.