Philipp Singer
ABSTRACT
Navigating websites represents a fundamental activity of users on the Web. Modeling this activity, i.e., understanding how predictable human navigation is and whether regularities can be detected has been of interest to researchers for nearly two decades. This is crucial for improving the Web experience of users by e.g., enhancing interfaces or information network structures. This thesis envisions to shedding light on human navigational patterns by trying to understand, leverage and improve human navigation on the Web. One main goal of this thesis is the construction of a versatile framework for modeling human navigational data with the use of Markov chains and for detecting the appropriate Markov chain order by using several advanced inference methods. It allows us to investigate memory and structure in human navigation patterns. Furthermore, we are interested in detecting whether pragmatic human navigational data can be leveraged by e.g., being useful for the task of calculating semantic relatedness between concepts. Finally, we want to find ways of enhancing human navigation models. Concretely, we plan on incorporating prior knowledge about the semantic relatedness between concepts to our Markov chain models as it is known that humans navigate the Web intuitively instead of randomly. Our experiments should be conducted on a variety of distinct navigational data including both goal oriented and free form navigation scenarios. We not only look at navigational paths over websites, but also abstract away to navigational paths over topics in order to get insights into cognitive patterns.
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Index Terms
- Understanding, leveraging and improving human navigation on the web
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