  Click-through rate(CTR) prediction is a core task in cost-per-click(CPC)
advertising systems and has been studied extensively by machine learning
practitioners. While many existing methods have been successfully deployed in
practice, most of them are built upon i.i.d.(independent and identically
distributed) assumption, ignoring that the click data used for training and
inference is collected through time and is intrinsically non-stationary and
drifting. This mismatch will inevitably lead to sub-optimal performance. To
address this problem, we formulate CTR prediction as a continual learning task
and propose COLF, a hybrid COntinual Learning Framework for CTR prediction,
which has a memory-based modular architecture that is designed to adapt, learn
and give predictions continuously when faced with non-stationary drifting click
data streams. Married with a memory population method that explicitly controls
the discrepancy between memory and target data, COLF is able to gain positive
knowledge from its historical experience and makes improved CTR predictions.
Empirical evaluations on click log collected from a major shopping app in China
demonstrate our method's superiority over existing methods. Additionally, we
have deployed our method online and observed significant CTR and revenue
improvement, which further demonstrates our method's efficacy.
