LaboUr - Machine Learning for User Modeling

User modeling [15,9] is concerned with acquisition and representation of assumptions about users of technical systems, and with the exploitation of these assumptions for system individualization. In many user modeling systems, the following shortcomings can be observed: Assumptions about mental attitudes like knowledge or goals are modeled, while behaviororiented assumptions, e.g. about interaction preferences or behavior patterns, are missing. Assumptions are acquired with specialized heuristics, which draw conclusions from isolated observations without regarding interaction context. User behavior, preferences, and mental attitudes are subject to change, which is often not treated adequately. User models are constructed and exploited mostly within the limits of one application. However, it can be beneficial to share information about users among several applications.