In the recommendation systems, there are multiple business domains to meet
the diverse interests and needs of users, and the click-through rate(CTR) of
each domain can be quite different, which leads to the demand for CTR
prediction modeling for different business domains. The industry solution is to
use domain-specific models or transfer learning techniques for each domain. The
disadvantage of the former is that the data from other domains is not utilized
by a single domain model, while the latter leverage all the data from different
domains, but the fine-tuned model of transfer learning may trap the model in a
local optimum of the source domain, making it difficult to fit the target
domain. Meanwhile, significant differences in data quantity and feature schemas
between different domains, known as domain shift, may lead to negative transfer
in the process of transferring. To overcome these challenges, we propose the
Collaborative Cross-Domain Transfer Learning Framework (CCTL). CCTL evaluates
the information gain of the source domain on the target domain using a
symmetric companion network and adjusts the information transfer weight of each
source domain sample using the information flow network. This approach enables
full utilization of other domain data while avoiding negative migration.
Additionally, a representation enhancement network is used as an auxiliary task
to preserve domain-specific features. Comprehensive experiments on both public
and real-world industrial datasets, CCTL achieved SOTA score on offline
metrics. At the same time, the CCTL algorithm has been deployed in Meituan,
bringing 4.37% CTR and 5.43% GMV lift, which is significant to the business.

该研究提出了一种基于协作跨域迁移学习框架（CCTL）的解决方案，可用于推荐系统中不同商业域中的广告点击率（CTR）预测建模，并成功应用于美团，提高了 CTR 和 GMV 的表现。