  We present a unified framework for Batch Online Learning (OL) for Click
Prediction in Search Advertisement. Machine Learning models once deployed, show
non-trivial accuracy and calibration degradation over time due to model
staleness. It is therefore necessary to regularly update models, and do so
automatically. This paper presents two paradigms of Batch Online Learning, one
which incrementally updates the model parameters via an early stopping
mechanism, and another which does so through a proximal regularization. We
argue how both these schemes naturally trade-off between old and new data. We
then theoretically and empirically show that these two seemingly different
schemes are closely related. Through extensive experiments, we demonstrate the
utility of of our OL framework; how the two OL schemes relate to each other and
how they trade-off between the new and historical data. We then compare batch
OL to full model retrains, and show how online learning is more robust to data
issues. We also demonstrate the long term impact of Online Learning, the role
of the initial Models in OL, the impact of delays in the update, and finally
conclude with some implementation details and challenges in deploying a real
world online learning system in production. While this paper mostly focuses on
application of click prediction for search advertisement, we hope that the
lessons learned here can be carried over to other problem domains.
