  Deep learning models in recommender systems are usually trained in the batch
mode, namely iteratively trained on a fixed-size window of training data. Such
batch mode training of deep learning models suffers from low training
efficiency, which may lead to performance degradation when the model is not
produced on time. To tackle this issue, incremental learning is proposed and
has received much attention recently. Incremental learning has great potential
in recommender systems, as two consecutive window of training data overlap most
of the volume. It aims to update the model incrementally with only the newly
incoming samples from the timestamp when the model is updated last time, which
is much more efficient than the batch mode training. However, most of the
incremental learning methods focus on the research area of image recognition
where new tasks or classes are learned over time. In this work, we introduce a
practical incremental method to train deep CTR models, which consists of three
decoupled modules (namely, data, feature and model module). Our method can
achieve comparable performance to the conventional batch mode training with
much better training efficiency. We conduct extensive experiments on a public
benchmark and a private dataset to demonstrate the effectiveness of our
proposed method.
