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2005
ACM

Web path recommendations based on page ranking and Markov models

8 years 11 months ago
Web path recommendations based on page ranking and Markov models
Markov models have been widely used for modelling users' navigational behaviour in the Web graph, using the transitional probabilities between web pages, as recorded in the web logs. The recorded users' navigation is used to extract popular web paths and predict current users’ next steps. Such purely usage-based probabilistic models, however, present certain shortcomings. Since the prediction of users' navigational behaviour is based solely on the usage data, structural properties of the Web graph are ignored. Thus important - in terms of pagerank authority score - paths may be underrated. In this paper we present a hybrid probabilistic predictive model extending the properties of Markov models by incorporating link analysis methods. More specifically, we propose the use of a PageRank-style algorithm for assigning prior probabilities to the web pages based on their importance in the web site's graph. We prove, through experimentation, that this approach results i...
Magdalini Eirinaki, Michalis Vazirgiannis, Dimitri
Added 26 Jun 2010
Updated 26 Jun 2010
Type Conference
Year 2005
Where WIDM
Authors Magdalini Eirinaki, Michalis Vazirgiannis, Dimitris Kapogiannis
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