Communications of the ACM
Web mining for web personalization
ACM Transactions on Internet Technology (TOIT)
User Modeling and User-Adapted Interaction
Similarity-Based Fuzzy Clustering for User Profiling
WI-IATW '07 Proceedings of the 2007 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Workshops
A Web Usage Mining Framework for Mining Evolving User Profiles in Dynamic Web Sites
IEEE Transactions on Knowledge and Data Engineering
Categorization of Web Users by Fuzzy Clustering
KES '08 Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part II
Modeling human behavior in user-adaptive systems: Recent advances using soft computing techniques
Expert Systems with Applications: An International Journal
User models for adaptive hypermedia and adaptive educational systems
The adaptive web
Data mining for web personalization
The adaptive web
Journal on Computing and Cultural Heritage (JOCCH)
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This paper focuses on modeling users' cognitive style based on a set of Web usage mining techniques on navigation patterns and clickstream data. Main aim is to investigate whether k-means clustering can group users of particular cognitive style using measures obtained from a series of psychometric tests and content navigation behavior. Three navigation metrics are proposed and used to find identifiable groups of users that have similar navigation patterns in relation to their cognitive style. The proposed work has been evaluated with a user study which entailed a psychometric-based method for extracting the users' cognitive styles, combined with a real usage scenario of users navigating in a controlled Web environment. A total of 22 participants of age between 20 and 25 participated in the reported study providing interesting insights with respect to cognitive styles and navigation behavior of users.