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Journal of Intelligent Information Systems

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01-08-2016

Improving the prediction of page access by using semantically enhanced clustering

Authors: Erman Sen, I. Hakki Toroslu, Pinar Karagoz

Published in: Journal of Intelligent Information Systems | Issue 1/2016

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Abstract

There are many parameters that may affect the navigation behaviour of web users. Prediction of the potential next page that may be visited by the web user is important, since this information can be used for prefetching or personalization of the page for that user. One of the successful methods for the determination of the next web page is to construct behaviour models of the users by clustering. The success of clustering is highly correlated with the similarity measure that is used for calculating the similarity among navigation sequences. This work proposes a new approach for determining the next web page by extending the standard clustering with the content-based semantic similarity method. Semantics of web-pages are represented as sets of concepts, and thus, user session are modelled as sequence of sets. As a result, session similarity is defined as an alignment of two sequences of sets. The success of the proposed method has been shown through applying it on real life web log data.

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Title: Improving the prediction of page access by using semantically enhanced clustering
Authors: Erman Sen
I. Hakki Toroslu
Pinar Karagoz
Publication date: 01-08-2016
Publisher: Springer US
Published in: Journal of Intelligent Information Systems / Issue 1/2016
Print ISSN: 0925-9902
Electronic ISSN: 1573-7675
DOI: https://doi.org/10.1007/s10844-016-0398-3

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