Title: Investigation of sequential pattern mining techniques for web recommendation

Authors: Thi Thanh Sang Nguyen; Hai Yan Lu; Tich Phuoc Tran; Jie Lu

Addresses: Decision Systems and e-Service Intelligence (DeSI) Lab, Centre for Quantum Computation and Intelligent Systems (QCIS), Faculty of Engineering and Information Technology, School of Software, University of Technology, Sydney, P.O. Box 123, Broadway, NSW 2007, Australia. ' Decision Systems and e-Service Intelligence (DeSI) Lab, Centre for Quantum Computation and Intelligent Systems (QCIS), Faculty of Engineering and Information Technology, School of Software, University of Technology, Sydney, P.O. Box 123, Broadway, NSW 2007, Australia. ' Centre for Real-Time Information Networks (CRIN), School of Computing and Communications, Faculty of Engineering and Information Technology, University of Technology, Sydney, P.O. Box 123, Broadway, NSW 2007, Australia. ' Decision Systems and e-Service Intelligence (DeSI) Lab, Centre for Quantum Computation and Intelligent Systems (QCIS), Faculty of Engineering and Information Technology, School of Software, University of Technology, Sydney, P.O. Box 123, Broadway, NSW 2007, Australia

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.

Keywords: web usage mining; web mining; sequence mining; sequential pattern mining; web recommendation; web recommender systems; WRS; web access sequence; frequent web access patterns.

DOI: 10.1504/IJIDS.2012.050378

International Journal of Information and Decision Sciences, 2012 Vol.4 No.4, pp.293 - 312

Published online: 09 Aug 2014 *

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