A sequential decision formulation of the interface card model for interactive IR

The Interface Card model is a promising new theoretical framework for modeling and optimizing interactive retrieval interfaces, but how to systematically instantiate it to solve concrete interface optimization problems remains an open challenge. We propose a novel formulation of the Interface Card model based on sequential decision theory, leading to a general framework for formal modeling of user states and stopping actions. The proposed framework naturally connects optimization of interactive retrieval with Markov Decision Processes and Partially Observable Markov Decision Processes, and enables the use of reinforcement learning algorithms for optimizing interactive retrieval interfaces. Simulation and user study experiments demonstrate the effectiveness of the proposed model in automatically adjusting the interface layout in adaptation to inferred user stopping tendencies in addition to user interaction and screen size.