Explore-Exploit: A Framework for Interactive and Online Learning
This work addresses the challenge of maintaining user experience in interactive systems for developers or practitioners, though it appears incremental as it builds on existing online learning concepts.
The paper tackles the problem of balancing exploration and exploitation in interactive user interfaces to avoid sub-optimal user experiences when options change frequently, presenting the Explore-Exploit framework that minimizes regressions and achieves efficiencies in online learning tasks.
Interactive user interfaces need to continuously evolve based on the interactions that a user has (or does not have) with the system. This may require constant exploration of various options that the system may have for the user and obtaining signals of user preferences on those. However, such an exploration, especially when the set of available options itself can change frequently, can lead to sub-optimal user experiences. We present Explore-Exploit: a framework designed to collect and utilize user feedback in an interactive and online setting that minimizes regressions in end-user experience. This framework provides a suite of online learning operators for various tasks such as personalization ranking, candidate selection and active learning. We demonstrate how to integrate this framework with run-time services to leverage online and interactive machine learning out-of-the-box. We also present results demonstrating the efficiencies that can be achieved using the Explore-Exploit framework.