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.

本研究侧重于具有显著差异的特征在多个领域中的分布和对模型预测的影响，称之为领域敏感特征，这些特征是领域区别的载体，对于多领域建模至关重要。为了避免忽视领域敏感特征，我们提出了一种能够识别最能反映领域区别的特征并从特征集中提取领域特定信息以增强建模的记忆架构。离线和在线实验证明了我们的方法在捕捉领域区别和提高多领域推荐性能方面的优越性。

多领域推荐的可检索领域敏感特征记忆