An Opportunistic Bandit Approach for User Interface Experimentation

TL;DR

This paper demonstrates how opportunistic bandit algorithms can optimize user interface testing in online retail by reducing costs and leveraging customer features, leading to more efficient experimentation.

Contribution

It introduces a novel application of opportunistic bandits to online retail UI experimentation, incorporating customer features to minimize exploration costs.

Findings

Significant regret reduction in UI testing costs.

Effective use of customer features to guide experiments.

Analysis of advantages and challenges of the approach.

Abstract

Facing growing competition from online rivals, the retail industry is increasingly investing in their online shopping platforms to win the high-stake battle of customer' loyalty. User experience is playing an essential role in this competition, and retailers are continuously experimenting and optimizing their user interface for better user experience. The cost of experimentation is dominated by the opportunity cost of providing a suboptimal service to the customers. Through this paper, we demonstrate the effectiveness of opportunistic bandits to make the experiments as inexpensive as possible using real online retail data. In fact, we model user interface experimentation as an opportunistic bandit problem, in which the cost of exploration varies under a factor extracted from customer features. We achieve significant regret reduction by mitigating costly exploration and providing extra contextual information that helps to guide the testing process. Moreover, we analyze the advantages and challenges of using opportunistic bandits for online retail experimentation.