Discovering valuable user behavior patterns in mobile commerce environments

Authors:
Bai-En Shie;Hui-Fang Hsiao;Philip S. Yu;Vincent S. Tseng
Affiliations:
Department of Computer Science and Information Engineering, National Cheng Kung University, Taiwan, ROC;Department of Computer Science and Information Engineering, National Cheng Kung University, Taiwan, ROC;Department of Computer Science, University of Illinois at Chicago, Chicago, Illinois;Department of Computer Science and Information Engineering, National Cheng Kung University, Taiwan, ROC
Venue:
PAKDD'11 Proceedings of the 15th international conference on New Frontiers in Applied Data Mining
Year:
2011

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Abstract

Mining user behavior patterns in mobile environments is an emerging topic in data mining fields with wide applications. By integrating moving paths with purchasing transactions, one can find the sequential purchasing patterns with the moving paths, which are called mobile sequential patterns of the mobile users. Mobile sequential patterns can be applied not only for planning mobile commerce environments but also analyzing and managing online shopping websites. However, unit profits and purchased numbers of the items are not considered in traditional framework of mobile sequential pattern mining. Thus, the patterns with high utility (i.e., profit here) cannot be found. In view of this, we aim at integrating mobile data mining with utility mining for finding high utility mobile sequential patterns in this study. A novel algorithm called UMSPL (high Utility Mobile Sequential Pattern mining by a Level-wised method) is proposed to efficiently find high utility mobile sequential patterns. The experimental results show that the proposed algorithm has excellent performance under various system conditions.