User modeling with sequential data

User models are useful to automatically adapt software or information to specific users. Because crafting user models by hand is labor intensive, machine learning techniques are used to construct user models. In this paper we illustrate how via instance based techniques user modeling tasks can be performed, based on sequences of observed user actions. We compare this approach with a common classification approach on real world data and briefly sketch how this technique is also useful when dealing with non-labeled data.