SUM-IML: Dynamic Scrutable User Modeling utilizing Interactive Machine Learning

Machine learning? is a powerful tool in modeling historic user data and converting it into a computational user model. However, it is nearly impossible for a machine learning process to have access to all data pertaining to a user. This leads to potential gaps in the user model produced. Thus, involving the user in overseeing and controlling the modelling process may be considered as a worthwhile goal. This form of user model control is often referred to as scrutability in the personalization research domain. The approach proposed in this research is to combine the benefits of machine learning driven user modelling and scrutable user control in the modelling process. This PhD is at its midpoint and is currently focused on building the model while taking into consideration how user feedback may be incorporated. This feedback is solicited iteratively, enabling the ML process to retrain with the benefit of the user input. The early results of the experimental work show promising prediction accuracy results.