Abstract
Categorization of users is a fundamental task in Web personalization. Fuzzy clustering is a valid approach to derive user categories by capturing similar user interests from web usage data available in log files. Usually, fuzzy clustering is based on the use of Euclidean metrics to evaluate similarity between user preferences. This can lead to user categories that do not capture the semantic information incorporated in the original Web usage data. To better capture similarity between users, in this paper we propose the use of a measure that is based on the evaluation of similarity between fuzzy sets. The proposed fuzzy measure is employed in a relational fuzzy clustering algorithm to discover clusters embedded in the Web usage data and derive categories modeling the preferences of similar users. An application example on usage data extracted from log files of a real Web site is reported and a comparison with the results obtained using the cosine measure is shown to demonstrate the effectiveness of the fuzzy similarity measure.
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Castellano, G., Torsello, M.A. (2008). Categorization of Web Users by Fuzzy Clustering. In: Lovrek, I., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2008. Lecture Notes in Computer Science(), vol 5178. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85565-1_28
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DOI: https://doi.org/10.1007/978-3-540-85565-1_28
Publisher Name: Springer, Berlin, Heidelberg
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