Abstract
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.
Highlights
Public domain web portals, for example, municipal websites, education, or healthcare services, are often organized into a hierarchy
We present the effect of the order for our dataset and the performance comparison between the Markov Model (MM), Long short-term memory (LSTM), multi-layer perceptron (MLP), and reinforcement learning (RL) models having dataset as an input containing random samples of the population of the year 2011
We assessed the performance of Markov, deep learning and Q-learning models in order to predict click events based on the clickstreams of a hierarchical website
Summary
For example, municipal websites, education, or healthcare services, are often organized into a hierarchy. When the content of a web page grows large, the information organization of the portals may not be intuitive for all users. They may find it laborious to reach the information they need or, in the worst case, do not find it at all. Recommendation of pages is a solution to minimize the user effort to find relevant information in a large information space. It is widely used in commercial websites but more sparsely in public domain portals
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
