An incremental collaborative filtering algorithm for recommender systems

Komkhao, M; Li, Z; Halang, WA; Lu, J

An incremental collaborative filtering algorithm for recommender systems

Komkhao, M Li, Z Halang, WA Lu, J

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Publication Type:: Conference Proceeding
Citation:: World Scientific Proc. Series on Computer Engineering and Information Science 7; Uncertainty Modeling in Knowledge Engineering and Decision Making - Proceedings of the 10th International FLINS Conf., 2012, 7 pp. 327 - 332
Issue Date:: 2012-12-01

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Field	Value	Language
dc.contributor.author	Komkhao, M	en_US
dc.contributor.author	Li, Z	en_US
dc.contributor.author	Halang, WA	en_US
dc.contributor.author	Lu, J https://orcid.org/0000-0003-0690-4732	en_US
dc.date.issued	2012-12-01	en_US
dc.identifier.citation	World Scientific Proc. Series on Computer Engineering and Information Science 7; Uncertainty Modeling in Knowledge Engineering and Decision Making - Proceedings of the 10th International FLINS Conf., 2012, 7 pp. 327 - 332	en_US
dc.identifier.isbn	9789814417730	en_US
dc.identifier.uri	http://hdl.handle.net/10453/31713
dc.description.abstract	Recommender systems are effective approaches to implement personalised e-services. In recent years, they have gained widespread applications in e-commerce. Current recommender systems still need, however, further improvements with respect to the accuracy of prediction and to solve the scalability problem. To this end, an incremental collaborative filtering (InCF) algorithm based on the Mahalanobis distance is presented for recommender systems. Furthermore, the Mahalanobis radial basis function with ellipsoidal shape is employed to determine the decision boundaries of clusters. Experimental results show that the algorithm proposed can lead to improved prediction accuracy and that it turns out to be scalable in recommender applications.	en_US
dc.relation.ispartof	World Scientific Proc. Series on Computer Engineering and Information Science 7; Uncertainty Modeling in Knowledge Engineering and Decision Making - Proceedings of the 10th International FLINS Conf.	en_US
dc.title	An incremental collaborative filtering algorithm for recommender systems	en_US
dc.type	Conference Proceeding
utslib.citation.volume	7	en_US
utslib.for	080108 Neural, Evolutionary and Fuzzy Computation	en_US
dc.location.activity	Istanbul, Turkey
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	7	en_US

Abstract:

Recommender systems are effective approaches to implement personalised e-services. In recent years, they have gained widespread applications in e-commerce. Current recommender systems still need, however, further improvements with respect to the accuracy of prediction and to solve the scalability problem. To this end, an incremental collaborative filtering (InCF) algorithm based on the Mahalanobis distance is presented for recommender systems. Furthermore, the Mahalanobis radial basis function with ellipsoidal shape is employed to determine the decision boundaries of clusters. Experimental results show that the algorithm proposed can lead to improved prediction accuracy and that it turns out to be scalable in recommender applications.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/31713