Dynamic method selection for profiling web users

Marko Ambrožič (2014) Dynamic method selection for profiling web users. EngD thesis.

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Abstract

User profiling is becoming an increasingly important subject in the field of web development as it enables improving the user experience through learning the users interests. In this study we examine dynamic selection of web user profiling methods. Our goal is to use machine learning methods to build a learning model that predicts the most successful combined profiling method, which is expected to be significantly better from each individual method. We have shown that combining of profiling methods using machine learning can be a powerful tool when looking for a way of improving the accuracy of web user profiles. We have also shown that dynamic selection is most effective when differences between profiling methods are relatively large and therefore providing room for improvement.

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