Prediction of user navigation patterns by mining the temporal web usage evolution

S. Tseng*, Kawuu Weicheng Lin, Jeng Chuan Chang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

13 Scopus citations

Abstract

Advances in the data mining technologies have enabled the intelligent Web abilities in various applications by utilizing the hidden user behavior patterns discovered from the Web logs. Intelligent methods for discovering and predicting user's patterns is important in supporting intelligent Web applications like personalized services. Although numerous studies have been done on Web usage mining, few of them consider the temporal evolution characteristic in discovering web user's patterns. In this paper, we propose a novel data mining algorithm named Temporal N-Gram (TN-Gram) for constructing prediction models of Web user navigation by considering the temporality property in Web usage evolution. Moreover, three kinds of new measures are proposed for evaluating the temporal evolution of navigation patterns under different time periods. Through experimental evaluation on both of real-life and simulated datasets, the proposed TN-Gram model is shown to outperform other approaches like N-gram modeling in terms of prediction precision, in particular when the web user's navigating behavior changes significantly with temporal evolution.

Original languageEnglish
Pages (from-to)157-163
Number of pages7
JournalSoft Computing
Volume12
Issue number2
DOIs
StatePublished - 1 Jan 2008

Keywords

  • Data mining
  • Navigation patterns
  • Personalized services
  • Temporal patterns

Fingerprint Dive into the research topics of 'Prediction of user navigation patterns by mining the temporal web usage evolution'. Together they form a unique fingerprint.

Cite this