  As one of the largest e-commerce platforms in the world, Taobao's
recommendation systems (RSs) serve the demands of shopping for hundreds of
millions of customers. Click-Through Rate (CTR) prediction is a core component
of the RS. One of the biggest characteristics in CTR prediction at Taobao is
that there exist multiple recommendation domains where the scales of different
domains vary significantly. Therefore, it is crucial to perform cross-domain
CTR prediction to transfer knowledge from large domains to small domains to
alleviate the data sparsity issue. However, existing cross-domain CTR
prediction methods are proposed for static knowledge transfer, ignoring that
all domains in real-world RSs are continually time-evolving. In light of this,
we present a necessary but novel task named Continual Transfer Learning (CTL),
which transfers knowledge from a time-evolving source domain to a time-evolving
target domain. In this work, we propose a simple and effective CTL model called
CTNet to solve the problem of continual cross-domain CTR prediction at Taobao,
and CTNet can be trained efficiently. Particularly, CTNet considers an
important characteristic in the industry that models has been continually
well-trained for a very long time. So CTNet aims to fully utilize all the
well-trained model parameters in both source domain and target domain to avoid
losing historically acquired knowledge, and only needs incremental target
domain data for training to guarantee efficiency. Extensive offline experiments
and online A/B testing at Taobao demonstrate the efficiency and effectiveness
of CTNet. CTNet is now deployed online in the recommender systems of Taobao,
serving the main traffic of hundreds of millions of active users.
