Elicitation of User Preferences via Incremental Learning in a Declarative Modelling Environment

Declarative Modelling environments exhibit an idiosyncrasy that demands specialised machine learning methodologies. The particular characteristics of the datasets, their irregularity in terms of class representation, volume, availability as well as user induced inconsistency further impede the learning potential of any employed mechanism, thus leading to the need for adaptation and adoption of custom approaches, expected to address these issues. In the current work we present the problems encountered in the effort to acquire and apply user profiles in such an environment, the modified boosting learning algorithm adopted and the corresponding experimental results.