  Feed recommendation is currently the mainstream mode for many real-world
applications (e.g., TikTok, Dianping), it is usually necessary to model and
predict user interests in multiple scenarios (domains) within and even outside
the application. Multi-domain learning is a typical solution in this regard.
While considerable efforts have been made in this regard, there are still two
long-standing challenges: (1) Accurately depicting the differences among
domains using domain features is crucial for enhancing the performance of each
domain. However, manually designing domain features and models for numerous
domains can be a laborious task. (2) Users typically have limited impressions
in only a few domains. Extracting features automatically from other domains and
leveraging them to improve the predictive capabilities of each domain has
consistently posed a challenging problem. In this paper, we propose an
Automatic Domain Feature Extraction and Personalized Integration (DFEI)
framework for the large-scale multi-domain recommendation. The framework
automatically transforms the behavior of each individual user into an
aggregation of all user behaviors within the domain, which serves as the domain
features. Unlike offline feature engineering methods, the extracted domain
features are higher-order representations and directly related to the target
label. Besides, by personalized integration of domain features from other
domains for each user and the innovation in the training mode, the DFEI
framework can yield more accurate conversion identification. Experimental
results on both public and industrial datasets, consisting of over 20 domains,
clearly demonstrate that the proposed framework achieves significantly better
performance compared with SOTA baselines. Furthermore, we have released the
source code of the proposed framework at https://github.com/xidongbo/DFEI.
