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Improving the prediction of page access by using semantically enhanced clustering

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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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Notes

  1. CLUTO (scluster, gcluto)—a cross-platform for clustering low- and high-dimensional datasets and for analyzing the characteristics of the various clusters, http://glaros.dtc.umn.edu/gkhome/cluto/cluto/overview

  2. One year of access web-logs of METU C.Eng. website (http://www.ceng.metu.edu.tr)

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Authors and Affiliations

  1. Computer Engineering Department, Middle East Technical University, 06800, Ankara, Turkey

    Erman Sen, I. Hakki Toroslu & Pinar Karagoz

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  1. Erman Sen
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  2. I. Hakki Toroslu
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  3. Pinar Karagoz
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Correspondence to Pinar Karagoz.

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Sen, E., Toroslu, I.H. & Karagoz, P. Improving the prediction of page access by using semantically enhanced clustering. J Intell Inf Syst 47, 165–192 (2016). https://doi.org/10.1007/s10844-016-0398-3

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  • DOI: https://doi.org/10.1007/s10844-016-0398-3

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