Efficient algorithms for discovering high utility user behavior patterns in mobile commerce environments

Bai En Shie, Hui Fang Hsiao, Vincent Shin-Mu Tseng

Research output: Contribution to journalArticle

29 Scopus citations

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 for 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. Two types of algorithms, namely level-wise and tree-based methods, are proposed for mining high-utility mobile sequential patterns. A series of analyses and comparisons on the performance of the two different types of algorithms are conducted through experimental evaluations. The results show that the proposed algorithms outperform the state-of-the-art mobile sequential pattern algorithms and that the tree-based algorithms deliver better performance than the level-wise ones under various conditions.

Original languageEnglish
Pages (from-to)363-387
Number of pages25
JournalKnowledge and Information Systems
Volume37
Issue number2
DOIs
StatePublished - 1 Nov 2013

Keywords

  • High-utility mobile sequential pattern
  • Mobile environments
  • Mobility pattern mining
  • Utility mining

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