Reinforcement Learning Page Prediction for Hierarchically Ordered Municipal Websites

Autor: Petri Puustinen, Kostas Stefanidis, Jaana Kekäläinen, Marko Junkkari
Jazyk: angličtina
Rok vydání: 2021
Předmět:
Zdroj: Information, Vol 12, Iss 6, p 231 (2021)
Druh dokumentu: article
ISSN: 2078-2489
DOI: 10.3390/info12060231
Popis: 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.
Databáze: Directory of Open Access Journals
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