A Perspective on Machine Learning and Comprehensibility from Knowledge Acquisition

on the knowledge that is to be learned. These determine the notion of comprehensibility. 1 Comprehensibility as generating appropriate explanations Some of the requirements on the knowledge that is to be learned are associated with knowledge as software. We want executable programs that can be read and understood by a programmer who may want to debug them or use them later as part of a new program. In this case criteria from software engineering such as modularity and size apply. A recent effort in the context of Inductive Logic Programming is the work by Sommer [1] on restructuring knowledge bases to give the call graph a uniform structure, without necessarily changing the functionality. However, general properties of the format of knowledge may help to make knowledge copmprehensible to a programmer, they will never be strong enough to specify "comprehensibility" for a user. We need an approach that gives us additional criteria. There is a vast literature

Validating Navigation Time Prediction Models for Menu Optimization

Authors of menu optimization methods often use navigation time prediction models without validating whether the model is adequate for the site and its users. We review the assumptions underlying navigation time prediction models and present a method to validate these assumptions offline. Experiments on four web sites show how accurate the various model features describe the behavior of the users. These results can be used to select the best model for a new optimization task. In addition, we find that the existing optimization methods all use suboptimal models. This indicates that our results can significantly contribute to more effective menu optimization