Learning to Persist: Exploring the Tradeoff Between Model Optimization and Experience Consistency
Dmitri Goldenberg, Guy Tsype, Igor Spivak, Javier Albert, Amir Tzur · Companion Proceedings of the Web Conference 2021 · 2021
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.