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Soft Computing

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01-04-2010 | Original Paper

Multiobjective evolutionary clustering of Web user sessions: a case study in Web page recommendation

Authors: G. Nildem Demir, A. Şima Uyar, Şule Gündüz-Öğüdücü

Published in: Soft Computing | Issue 6/2010

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Abstract

In this study, we experiment with several multiobjective evolutionary algorithms to determine a suitable approach for clustering Web user sessions, which consist of sequences of Web pages visited by the users. Our experimental results show that the multiobjective evolutionary algorithm-based approaches are successful for sequence clustering. We look at a commonly used cluster validity index to verify our findings. The results for this index indicate that the clustering solutions are of high quality. As a case study, the obtained clusters are then used in a Web recommender system for representing usage patterns. As a result of the experiments, we see that these approaches can successfully be applied for generating clustering solutions that lead to a high recommendation accuracy in the recommender model we used in this paper.

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Available only for authorised users

This term will be used interchangeably with “user session”.

http://www.tik.ee.ethz.ch/sop/pisa/

The path that has the highest similarity to the active user session is defined as the best matching path.

http://www.bidb.itu.edu.tr/

http://www.ce.itu.edu.tr/

http://ita.ee.lbl.gov/html/contrib/Sask-HTTP.html

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http://www.w3.org/

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Title: Multiobjective evolutionary clustering of Web user sessions: a case study in Web page recommendation
Authors: G. Nildem Demir
A. Şima Uyar
Şule Gündüz-Öğüdücü
Publication date: 01-04-2010
Publisher: Springer-Verlag
Published in: Soft Computing / Issue 6/2010
Print ISSN: 1432-7643
Electronic ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-009-0428-y

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