Reinforcement learning page prediction for hierarchically ordered municipal websites

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Public websites offer information on a variety of topics and services and are accessed by users with varying skills to browse the kind of electronic document repositories. However, the complex website structure and diversity of web browsing behavior create a challenging task for click prediction. This paper presents the results of a novel reinforcement learning approach to model user browsing patterns in a hierarchically ordered municipal website. We study how accurate predictor the browsing history is, when the target pages are not immediate next pages pointed by hyperlinks, but appear a number of levels down the hierarchy. We compare traditional type of baseline classifiers’ performance against our reinforcement learning-based training algorithm.

Original languageEnglish
Article number231
JournalInformation (Switzerland)
Issue number6
Publication statusPublished - 28 May 2021
Publication typeA1 Journal article-refereed


  • Clickstream analysis
  • Deep learning
  • Hierarchically ordered website
  • Markov model
  • Q-learning
  • Reinforcement learning

Publication forum classification

  • Publication forum level 0

ASJC Scopus subject areas

  • Information Systems


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