Retrievable Domain-Sensitive Feature Memory for Multi-Domain Recommendation

TL;DR

This paper introduces a novel approach for multi-domain recommendation that emphasizes the importance of domain-sensitive features, using a memory architecture to better capture domain distinctions and improve recommendation accuracy.

Contribution

It proposes a domain-sensitive feature attribution method and a memory architecture to enhance multi-domain recommendation by explicitly modeling domain distinctions.

Findings

Improved recommendation performance in multiple domains.

Effective identification of domain-sensitive features.

Enhanced modeling of domain distinctions.

Abstract

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.