Optimizing Interactive Systems via Data-Driven Objectives

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). G...

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Bibliographic Details
Published inarXiv.org
Main Authors Li, Ziming, Kiseleva, Julia, Agarwal, Alekh, de Rijke, Maarten, White, Ryen W
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 19.06.2020
Subjects
Algorithms
Computer simulation
Interactive systems
Objectives
Optimization
System effectiveness
User behavior
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Summary: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.
ISSN:2331-8422