Optimizing Interactive Systems via Data-Driven Objectives
This addresses the problem of manually crafted objectives in interactive systems, offering a general method for improved user experience, though it appears incremental as it builds on existing optimization techniques.
The paper tackles the challenge of optimizing interactive systems by proposing a data-driven approach to infer objectives from user interactions, demonstrating high effectiveness in simulations.
Effective optimization is essential for real-world interactive systems to provide a satisfactory user experience in response to changing user behavior. However, it is often challenging to find an objective to optimize for interactive systems (e.g., policy learning in task-oriented dialog systems). Generally, such objectives are manually crafted and rarely capture complex user needs in an accurate manner. We propose an approach that infers the objective directly from observed user interactions. These inferences can be made regardless of prior knowledge and across different types of user behavior. We introduce Interactive System Optimizer (ISO), a novel algorithm that uses these inferred objectives for optimization. Our main contribution is a new general principled approach to optimizing interactive systems using data-driven objectives. We demonstrate the high effectiveness of ISO over several simulations.