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Can user models be learned at all? Inherent problems in machine learning for user modelling

Martin E. Müller

erschienen 2004 "Knowledge Engineering Review", Issue 1, Volume 19, Pages 61 - 88

Verlag: Cambridge University Press

DOI: ACM.org


Abstract:

Machine learning seems to offer the solution to many problems in user modelling. However, one tends to run into similar problems each time one tries to apply out-of-the-box solutions to machine learning. This article closely relates the user modelling problem to the machine learning problem. It explicates some inherent dilemmas that are likely to be overlooked when applying machine learning algorithms in user modelling. Some examples illustrate how specific approaches deliver satisfying results and discuss underlying assumptions on the domain or how learned hypotheses relate to the requirements on the user model. Finally, some new or underestimated approaches offering promising perspectives in combined systems are discussed. The article concludes with a tentative ‘‘checklist” that one might like to consider when planning to apply machine learning to user modelling techniques.


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