  One of the key challenges of learning an online recommendation model is the
temporal domain shift, which causes the mismatch between the training and
testing data distribution and hence domain generalization error. To overcome,
we propose to learn a meta future gradient generator that forecasts the
gradient information of the future data distribution for training so that the
recommendation model can be trained as if we were able to look ahead at the
future of its deployment. Compared with Batch Update, a widely used paradigm,
our theory suggests that the proposed algorithm achieves smaller temporal
domain generalization error measured by a gradient variation term in a local
regret. We demonstrate the empirical advantage by comparing with various
representative baselines.
