BIGexplore: Bayesian Information Gain Framework for Information Exploration

Abstract

The Bayesian information gain (BIG) framework has garnered significant interest as an interaction method for predicting a user's intended target based on a user's input. However, the BIG framework is constrained to goal-oriented cases, which renders it difficult to support changing goal-oriented cases such as design exploration. During the design exploration process, the design direction is often undefined and may vary over time. The designer's mental model specifying the design direction is sequentially updated through the information-retrieval process. Therefore, tracking the change point of a user's goal is crucial for supporting an information exploration. We introduce the BIGexplore framework for changing goal-oriented cases. BIGexplore detects transitions in a user's browsing behavior as well as the user's next target. Furthermore, a user study on BIGexplore confirms that the computational cost is significantly reduced compared with the existing BIG framework, and it plausibly detects the point where the user changes goals.