Combining User and Usage Modelling for User-Adaptivity Systems

User-adapted interaction has been a research goal for many years now. In research on natural-language dialog systems, user models were introduced as explicit representation of assumptions about the individual characteristics of users. Systems would consult these models to decide how to adapt their behaviour to each user. Traditional user models contain mentalistic assumptions (MA), i.e., assumptions about knowledge, goals, interests and other mental attitudes. They are typically represented explicitly in some symbolic or numeric format (Pohl 1998). The main problem in this traditional approach is how to acquire relevant assumptions about the user. In most systems, heuristics were used to control user model acquisition, which often led to unreliable system adaptivity.