Learning to Persist: Exploring the Tradeoff Between Model Optimization and Experience Consistency

Machine learning models and recommender systems play a crucial role in web applications, providing personalized experiences to each customer. Recurring visits of the same customer raise a nontrivial question about the persistence of the experience. Given a changing user context, alongside online algorithms that update over time, the optimal treatment might differ from past model decisions. However, changing customer experience may create inconsistency and harm customer satisfaction and business process completion. This paper discusses the tradeoff between providing the user with a consistent experience and suggesting an up-to-date optimal treatment. We offer preliminary approaches to tackle the persistence problem and explore the tradeoffs in a simulated study.