  With the increase in the business scale and number of domains in online
advertising, multi-domain ad recommendation has become a mainstream solution in
the industry. The core of multi-domain recommendation is effectively modeling
the commonalities and distinctions among domains. Existing works are dedicated
to designing model architectures for implicit multi-domain modeling while
overlooking an in-depth investigation from a more fundamental perspective of
feature distributions. This paper focuses on features with significant
differences across various domains in both distributions and effects on model
predictions. We refer to these features as domain-sensitive features, which
serve as carriers of domain distinctions and are crucial for multi-domain
modeling. Experiments demonstrate that existing multi-domain modeling methods
may neglect domain-sensitive features, indicating insufficient learning of
domain distinctions. To avoid this neglect, we propose a domain-sensitive
feature attribution method to identify features that best reflect domain
distinctions from the feature set. Further, we design a memory architecture
that extracts domain-specific information from domain-sensitive features for
the model to retrieve and integrate, thereby enhancing the awareness of domain
distinctions. Extensive offline and online experiments demonstrate the
superiority of our method in capturing domain distinctions and improving
multi-domain recommendation performance.
