Naive Bayes Classifiers for User Modeling

In this paper we discuss how machine learning, and specifical ly how naive Bayes clas- sifiers, can be used for user modeling tasks. We argue that in g eneral, 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 ap proach 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 mod- eling systems and how this technique can be applied to them. Finally, we propose a set of enhancements to naive Bayes classifiers to improve their p redictive 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 othe r than a list of potentially useful features.