Naive Bayes Classifiers for User Modeling

In this paper we discuss how machine learning, and specifically how naive Bayes classifiers, can be used for user modeling tasks. We argue that in general, machine learning techniques should be used to improve a user modeling system’s interactions with users. We further argue that a naive Bayes classifier is a reasonable approach to many user modeling problems, given its advantages of quick learning and low computational overhead. These are critical features for an online user modeling system. We discuss two such user modeling systems and how this technique can be applied to them. Finally, we propose a set of enhancements to naive Bayes classifiers to improve their predictive accuracy, and allow them to better adapt to the user’s performance. Our eventual goal is to construct a learning system that requires no intervention from the designer other than a list of potentially useful features.