文章基本信息

标题：Reinforcement Learning Page Prediction for Hierarchically Ordered Municipal Websites
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作者：Petri Puustinen ; Kostas Stefanidis ; Jaana Kekäläinen 等
期刊名称：Information
电子版ISSN：2078-2489
出版年度：2021
卷号：12
期号：6
页码：231
DOI：10.3390/info12060231
出版社：MDPI Publishing
摘要：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.
关键词：clickstream analysis; markov model; deep learning; reinforcement learning; Q-learning; hierarchically ordered website clickstream analysis ; markov model ; deep learning ; reinforcement learning ; Q-learning ; hierarchically ordered website