Building a Stochastic Dynamic Model of Application Use
This addresses the labor-intensive process of model development for application developers, though it appears incremental as it refines existing state-space methods.
The paper tackles the problem of building and maintaining user and application models for intelligent interfaces, which is time-consuming, by automatically constructing a stochastic dynamic model from observed user interactions. They evaluate their approach using real-world application usage data.
Many intelligent user interfaces employ application and user models to determine the user's preferences, goals and likely future actions. Such models require application analysis, adaptation and expansion. Building and maintaining such models adds a substantial amount of time and labour to the application development cycle. We present a system that observes the interface of an unmodified application and records users' interactions with the application. From a history of such observations we build a coarse state space of observed interface states and actions between them. To refine the space, we hypothesize sub-states based upon the histories that led users to a given state. We evaluate the information gain of possible state splits, varying the length of the histories considered in such splits. In this way, we automatically produce a stochastic dynamic model of the application and of how it is used. To evaluate our approach, we present models derived from real-world application usage data.