Intelligent techniques for recommender systems


Autoria(s): Ren, Yongli
Contribuinte(s)

Zhou, Wanlei

Li, Gang

Data(s)

01/07/2013

Resumo

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.

Identificador

http://hdl.handle.net/10536/DRO/DU:30062528

Idioma(s)

eng

Publicador

Deakin University, Faculty of Science, Engineering and Built Environment, School of Information Technology

Relação

http://dro.deakin.edu.au/eserv/DU:30062528/ren-agreement-2013.pdf

http://dro.deakin.edu.au/eserv/DU:30062528/ren-intelligenttechniques-2013A.pdf

Direitos

The Author. All Rights Reserved

Palavras-Chave #Recommender systems #Collaborative filtering #Imputation techniques #Low-rank subspace techniques #Optimizations techniques #Rating pattern subspace
Tipo

Thesis