Using Partially-Ordered Sequential Rules to Generate More Accurate Sequence Prediction

  • Philippe Fournier-Viger
  • Ted Gueniche
  • Vincent S. Tseng
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7713)

Abstract

Predicting the next element(s) of a sequence is a research problem with wide applications such as stock market prediction, consumer product recommendation, and web link recommendation. To address this problem, an effective approach is to mine sequential rules from a set of training sequences to then use these rules to make predictions for new sequences. In this paper, we improve on this approach by proposing to use a new kind of sequential rules named partially-ordered sequential rules instead of standard sequential rules. Experiments on large click-stream datasets for webpage recommendation show that using this new type of sequential rules can greatly increase prediction accuracy, while requiring a smaller training set.

Keywords

symbolic sequence prediction sequential rules partial order 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Philippe Fournier-Viger
    • 1
  • Ted Gueniche
    • 1
  • Vincent S. Tseng
    • 2
  1. 1.Dept. of Computer ScienceUniversity of MonctonCanada
  2. 2.Dept. of Computer Science and Inf. Eng.National Cheng Kung UniversityTaiwan

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