  Cross-domain recommendation (CDR) is an important method to improve
recommender system performance, especially when observations in target domains
are sparse. However, most existing cross-domain recommendations fail to fully
utilize the target domain's special features and are hard to be generalized to
new domains. The designed network is complex and is not suitable for rapid
industrial deployment. Our method introduces a two-step domain-aware
cross-attention, extracting transferable features of the source domain from
different granularity, which allows the efficient expression of both domain and
user interests. In addition, we simplify the training process, and our model
can be easily deployed on new domains. We conduct experiments on both public
datasets and industrial datasets, and the experimental results demonstrate the
effectiveness of our method. We have also deployed the model in an online
advertising system and observed significant improvements in both
Click-Through-Rate (CTR) and effective cost per mille (ECPM).
