Modeling of Human Web Browsing Based on Theory of Interest-Driven Behavior
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The ability to generate human-like Web-browsing requests is essential for testing and optimization of WWW systems. In this thesis a new model of human-browsing behavior the so-called HBB-IDT model has been proposed. The model is based on the theory of interest-driven human behavior and does not assume the availability of server-side logs (i.e., previous browsing history). The defining features of the model are: (1) human browsing on the internet is regarded as a dynamic interest-driven process; and (2) the users browsing interests are linked to actual characteristics of the visited Web pages. Given that the model does not rely on the existence of Web logs, it can be applied more generally than the previously proposed data-mining approaches. The experimental results show that the probability of generating human-like browsing sequences is much higher using the HBB-IDT model than using the pre-set request list model or the random crawling model.