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Improving Mobile Web Navigation Using N-Grams Prediction Models

Improving Mobile Web Navigation Using N-Grams Prediction Models
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Author(s): Yongjian Fu (Cleveland State University, USA), Hironmoy Paul (Cleveland State University, USA) and Namita Shetty (Cleveland State University, USA)
Copyright: 2009
Pages: 13
Source title: Mobile Computing: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): David Taniar (Monash University, Australia)
DOI: 10.4018/978-1-60566-054-7.ch223

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Abstract

In this article; we propose to use N-gram models for improving Web navigation for mobile users. N-gram models are built from Web server logs to learn navigation patterns of mobile users. They are used as prediction models in an existing algorithm which improves mobile Web navigation by recommending shortcuts. Our experiments on two real data sets show that N-gram models are as effective as other more complex models in improving mobile Web navigation.

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