Recommender systems are an essential component of online systems, providing users with a personalized experience. Some recommendation scenarios such as social networks or news are very dynamic, with new items added continuously and the interest of users changing over time due to breaking news or popular events. Incremental training is a popular technique to keep recommender models up-to-date in those dynamic platforms. In this paper, we provide an empirical analysis of a large industry dataset from the Sharechat app MOJ, a social media platform for short videos, to answer relevant questions like – how often should I retrain the model? – do different models, features and dataset sizes benefit from incremental training? – Do all users and items benefit the same from incremental training?