User modeling - A notoriously black art


This paper is intended as guidance for those who are familiar with user modeling field but are less fluent in statistical methods. It addresses potential problems with user model selection and evaluation, that are often clear to expert modelers, but are not obvious for others. These problems are frequently a result of a falsely straightforward application of statistics to user modeling (e.g. over-reliance on model fit metrics). In such cases, absolute trust in arguably shallow model accuracy measures could lead to selecting models that are hard-to-interpret, less meaningful, over-fit, and less generalizable. We offer a list of questions to consider in order to avoid these modeling pitfalls. Each of the listed questions is backed by an illustrative example based on the user modeling approach called Performance Factors Analysis (PFA) [9]. © 2011 Springer-Verlag.

Publication Title

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)