Machine learning and knowledge representation in the LaboUr approach to user modeling

In early user-adaptive systems, the use of knowledge representation methods for user modeling has often been the focus of research. In recent years, however, the application of machine learning techniques to control user-adapted interaction has become popular. In this paper, we present and compare adaptive systems that use either knowledge representation or machine learning for user modeling. Based on this comparison, several dimensions are identified that can be used to distinguish both approaches, but also to characterize user modeling systems in general. The LaboUr (Learning about the User) approach to user modeling is presented which attempts to take an ideal position in the resulting multi-dimensional space by combining machine learning and knowledge representation techniques. Finally, an implementation of LaboUr ideas into the information server ELFI is sketched.

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