Adaptation of the user navigation scheme using clustering and frequent pattern mining techiques for profiling

Abstract

There is a need to facilitate access to the required information in the web and adapting it to the users' preferences and requirements. This paper presents a system that, based on a collaborative filtering approach, adapts the web site to improve the browsing experience of the user: it generates automatically interesting links for new users. The system only uses the web log files stored in any web server (common log format) and builds user profiles from them combining machine learning techniques with a generalization process for data representation. These profiles are later used in an exploitation stage to automatically propose links to new users. The paper examines the effect of the parameters of the system on its final performance. Experiments show that the designed system performs efficiently in a database accessible from the web and that the use of a generalization process, specificity in profiles and the use of frequent pattern mining techniques benefit the profile generation phase, and, moreover, diversity seems to help in the exploitation phase.