An ambient software monitoring system for unsupervised user modelling
This paper describes a means of unsupervised learning of recurring patterns in user activity through patterns in system level events generated by a graphical user interface. Earlier work has shown that using this distillation of the more complex behavioural interaction between the user and the application provides a symbolic representation of knowledge and goals that could be used to imply preference. Although prior research has explored the possibilities of removing this information acquisition bottleneck in such an expert system using ambient monitoring approaches, some have experienced difficulty in dealing with the varying length training sequences and segmentation of the continuous event stream. Unlike previous work the approach documented here handles interactions of varying sizes and is able to recall recurrent patterns in real time irrespective of the number of interactions learned. In addition to describing the proposed approach we also describe the shortcomings of various previously applied machine learning techniques on the same type of data. We also demonstrate a practical implementation of our approach applied to web browser usage.
user interface finite state machines pattern discovery behaviour modelling gesture recognition human computer interaction