Investigation of sequential pattern mining techniques for web recommendation

Publication Type:
Journal Article
Citation:
International Journal of Information and Decision Sciences, 2012, 4 (4), pp. 293 - 312
Issue Date:
2012-01-01
Full metadata record
Files in This Item:
Filename Description Size
ijids.2012.050378.pdfPublished Version578.38 kB
Adobe PDF
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.
Please use this identifier to cite or link to this item: