Investigation of sequential pattern mining techniques for web recommendation

Nguyen, TTS; Lu, HY; Tran, TP; Lu, J

Investigation of sequential pattern mining techniques for web recommendation

Nguyen, TTS Lu, HY

Tran, TP Lu, J

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Publication Type:: Journal Article
Citation:: International Journal of Information and Decision Sciences, 2012, 4 (4), pp. 293 - 312
Issue Date:: 2012-01-01

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Field	Value	Language
dc.contributor.author	Nguyen, TTS	en_US
dc.contributor.author	Lu, HY https://orcid.org/0000-0001-5655-0237	en_US
dc.contributor.author	Tran, TP	en_US
dc.contributor.author	Lu, J https://orcid.org/0000-0003-0690-4732	en_US
dc.date.issued	2012-01-01	en_US
dc.identifier.citation	International Journal of Information and Decision Sciences, 2012, 4 (4), pp. 293 - 312	en_US
dc.identifier.issn	1756-7017	en_US
dc.identifier.uri	http://hdl.handle.net/10453/114913
dc.description.abstract	Increased application of sequence mining in web recommender systems (WRS) requires a better understanding of the performance and a clear identification of the strengths and weaknesses of existing algorithms. Among the commonly used sequence mining methods, the tree-based approach, such as pre-order linked WAP-tree mining algorithm (PLWAP-Mine) and conditional sequence mining algorithm (CS-Mine), has demonstrated high performance in web mining applications. However, its advantages over other mining methods are not well explained and understood in the context of WRS. This paper firstly reviews the existing sequence mining algorithms, and then studies the performance of two outstanding algorithms, i.e., the PLWAP-Mine and CS-Mine algorithms, with respect to their sensitivity to the dataset variability, and their practicality for web recommendation. The results show that CS-Mine performs faster than PLWAP-Mine, but the frequent patterns generated by PLWAP-Mine are more effective than CS-Mine when applied in web recommendations. These results are useful to WRS developers for the selection of appropriate sequence mining algorithms. © 2012 Inderscience Enterprises Ltd.	en_US
dc.relation.ispartof	International Journal of Information and Decision Sciences	en_US
dc.relation.isbasedon	10.1504/IJIDS.2012.050378	en_US
dc.title	Investigation of sequential pattern mining techniques for web recommendation	en_US
dc.type	Journal Article
utslib.citation.volume	4	en_US
utslib.for	0806 Information Systems	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
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/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Systems, Management and Leadership
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.issue	4	en_US
pubs.publication-status	Published	en_US
pubs.volume	4	en_US

Abstract:

Increased application of sequence mining in web recommender systems (WRS) requires a better understanding of the performance and a clear identification of the strengths and weaknesses of existing algorithms. Among the commonly used sequence mining methods, the tree-based approach, such as pre-order linked WAP-tree mining algorithm (PLWAP-Mine) and conditional sequence mining algorithm (CS-Mine), has demonstrated high performance in web mining applications. However, its advantages over other mining methods are not well explained and understood in the context of WRS. This paper firstly reviews the existing sequence mining algorithms, and then studies the performance of two outstanding algorithms, i.e., the PLWAP-Mine and CS-Mine algorithms, with respect to their sensitivity to the dataset variability, and their practicality for web recommendation. The results show that CS-Mine performs faster than PLWAP-Mine, but the frequent patterns generated by PLWAP-Mine are more effective than CS-Mine when applied in web recommendations. These results are useful to WRS developers for the selection of appropriate sequence mining algorithms. © 2012 Inderscience Enterprises Ltd.

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http://hdl.handle.net/10453/114913