Context-Sensitive Modeling of Web-Surfing Behaviour using Concept Trees

Early approaches to mathematically abstracting websurfing behavior were largely based on first-order Markov models. Most humans however do not surf in a “memoryless” fashion, rather they are guided by their timedependent situational context and associated information needs. This belief is corroborated by the non-exponential revisit times observed in many site-centric weblogs. In this paper, we propose a general framework for modeling users whose surfing behavior is dynamically governed by their current topic of interest. This allows a modeled surfer to behave differently on the same page, depending on his situational context. The proposed methodology involves mapping each visited page to a topic or concept, (conceptually) imposing a tree hierarchy on these topics, and then estimating the parameters of a semi-Markov process defined on this tree based on the observed transitions among the underlying visited pages. The semi-Markovian assumption imparts additional flexibility by allowing for non-exponential state re-visit times, and the concept hierarchy provides a nice way of capturing context and user intent. Our approach is computationally much less demanding as compared to the alternative approach of using higher order Markov models for capturing history-sensitive surfing behavior. Several practical applications are described. The application of better predicting which outlink a surfer may take, is illustrated using web-log data from a rich community portal, www.sulekha.com as an example, though the focus of the paper is on forming a plausible generative model rather than solving any specific task.