  In this paper we propose to solve an important problem in recommendation --
user cold start, based on meta leaning method. Previous meta learning
approaches finetune all parameters for each new user, which is both computing
and storage expensive. In contrast, we divide model parameters into fixed and
adaptive parts and develop a two-stage meta learning algorithm to learn them
separately. The fixed part, capturing user invariant features, is shared by all
users and is learned during offline meta learning stage. The adaptive part,
capturing user specific features, is learned during online meta learning stage.
By decoupling user invariant parameters from user dependent parameters, the
proposed approach is more efficient and storage cheaper than previous methods.
It also has potential to deal with catastrophic forgetting while continually
adapting for streaming coming users.
  Experiments on production data demonstrates that the proposed method
converges faster and to a better performance than baseline methods.
Meta-training without online meta model finetuning increases the AUC from
72.24% to 74.72% (2.48% absolute improvement). Online meta training achieves a
further gain of 2.46\% absolute improvement comparing with offline meta
training.
