  In industrial recommendation systems, there are several mini-apps designed to
meet the diverse interests and needs of users. The sample space of them is
merely a small subset of the entire space, making it challenging to train an
efficient model. In recent years, there have been many excellent studies
related to cross-domain recommendation aimed at mitigating the problem of data
sparsity. However, few of them have simultaneously considered the adaptability
of both sample and representation continual transfer setting to the target
task. To overcome the above issue, we propose a Entire space Continual and
Adaptive Transfer learning framework called ECAT which includes two core
components: First, as for sample transfer, we propose a two-stage method that
realizes a coarse-to-fine process. Specifically, we perform an initial
selection through a graph-guided method, followed by a fine-grained selection
using domain adaptation method. Second, we propose an adaptive knowledge
distillation method for continually transferring the representations from a
model that is well-trained on the entire space dataset. ECAT enables full
utilization of the entire space samples and representations under the
supervision of the target task, while avoiding negative migration.
Comprehensive experiments on real-world industrial datasets from Taobao show
that ECAT advances state-of-the-art performance on offline metrics, and brings
+13.6% CVR and +8.6% orders for Baiyibutie, a famous mini-app of Taobao.
