NDLI: Web path recommendations based on page ranking and Markov models

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Web path recommendations based on page ranking and Markov models

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Web path recommendations based on page ranking and Markov models

Content Provider	ACM Digital Library
Author	Eirinaki, Magdalini Kapogiannis, Dimitris Vazirgiannis, Michalis
Abstract	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 in more objective and representative predictions than the ones produced from the pure usage-based approaches.
Starting Page	2
Ending Page	9
Page Count	8
File Format	PDF
ISBN	1595931945
DOI	10.1145/1097047.1097050
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2005-11-04
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Link analysis Web personalization Pagerank Markov models
Content Type	Text
Resource Type	Article

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