Mining interesting user behavior patterns in mobile commerce environments

  • Authors:

  • Bai-En Shie;Philip S. Yu;Vincent S. Tseng

  • Affiliations:

  • Department of Computer Science and Information Engineering, National Cheng Kung University, Taiwan, ROC;Department of Computer Science, University of Illinois at Chicago, Chicago, USA;Department of Computer Science and Information Engineering, National Cheng Kung University, Taiwan, ROC

  • Venue:
  • Applied Intelligence
  • Year:
  • 2013

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Abstract

Discovering user behavior patterns from mobile commerce environments is an essential topic with wide applications, such as planning physical shopping sites, maintaining e-commerce on mobile devices and managing online shopping websites. Mobile sequential pattern mining is an emerging issue in this topic, which considers users' moving paths and purchased items in mobile commerce environments to find the complete set of mobile sequential patterns. However, an important factor, namely users' interests, has not been considered yet in past studies. In practical applications, users may only be interested in the patterns with some user-specified constraints. The traditional methods without considering the constraints pose two crucial problems: (1) Users may need to filter out uninteresting patterns within huge amount of patterns, (2) Finding the complete set of patterns containing the uninteresting ones needs high computational cost and runtime. In this paper, we address the problem of mining mobile sequential patterns with two kinds of constraints, namely importance constraints and pattern constraints. Here, we consider the importance of an item as its utility (i.e., profit) in the mobile commerce environment. An efficient algorithm, IM-Span (I nteresting M obile S equential Pa tter n mining), is proposed for dealing with the two kinds of constraints. Several effective strategies are employed to reduce the search space and computational cost in different aspects. Experimental results show that the proposed algorithms outperform state-of-the-art algorithms significantly under various conditions.