A note on the advantage of context in Thompson sampling

Abstract

Personalization has become a focal point of modern revenue management. However, it is often the case that minimal data are available to appropriately make suggestions tailored to each customer. This has led to many products making use of reinforcement learning-based algorithms to explore sets of offerings to find the best suggestions to improve conversion and revenue. Arguably the most popular of these algorithms are built on the foundation of the multi-arm bandit framework, which has shown great success across a variety of use cases. A general multi-arm bandit algorithm aims to trade-off adaptively exploring available, but under observed, recommendations, with the current known best offering. While much success has been achieved with these relatively understandable procedures, much of the airline industry is losing out on better personalized offers by ignoring the context of the transaction, as is the case in the traditional multi-arm bandit setup. Here, we explore a popular exploration heuristic, Thompson sampling, and note implementation details for multi-arm and contextual bandit variants. While the contextual bandit requires greater computational and technical complexity to include contextual features in the decision process, we illustrate the value it brings by the improvement in overall expected