The quality and clarity of the information that is collected from the user determines the suitability of user models primarily. Studies on user modeling have a serious problem because of the insufficiency of the data, poor application of the methodologies, noise in the data, and imprecise nature of human behavior. User modeling should be done in a proper manner, i.e., by adopting the most relevant technique for the intended domain, in order to get the best results. Soft computing and machine learning Techniques are frequently employed for user modeling because they have the capacity to deal with ambiguity. In this article, several user modeling methodologies are reviewed, and the machine learning and soft computing techniques that have effectively captured and formally represented human behavior are critically analyzed.

Soft Computing, Machine Learning, Overview Learning, and Classifications.