  We propose a method to easily modify existing offline Recommender Systems to
run online using Transfer Learning. Online Learning for Recommender Systems has
two main advantages: quality and scale. Like many Machine Learning algorithms
in production if not regularly retrained will suffer from Concept Drift. A
policy that is updated frequently online can adapt to drift faster than a batch
system. This is especially true for user-interaction systems like recommenders
where the underlying distribution can shift drastically to follow user
behaviour. As a platform grows rapidly like Grubhub, the cost of running batch
training jobs becomes material. A shift from stateless batch learning offline
to stateful incremental learning online can recover, for example, at Grubhub,
up to a 45x cost savings and a +20% metrics increase. There are a few
challenges to overcome with the transition to online stateful learning, namely
convergence, non-stationary embeddings and off-policy evaluation, which we
explore from our experiences running this system in production.
