D3: A Methodological Exploration of Domain Division, Modeling, and Balance in Multi-Domain Recommendations

Abstract

To enhance the efficacy of multi-scenario services in industrial recommendation systems, the emergence of multi-domain recommendation has become prominent, which entails simultaneous modeling of all domains through a unified model, effectively capturing commonalities and differences among them. However, current methods rely on manual domain partitioning, which overlook the intricate domain relationships and the heterogeneity of different domains during joint optimization, hindering the integration of domain commonalities and differences. To address these challenges, this paper proposes a universal and flexible framework D3 aimed at optimizing the multi-domain recommendation pipeline from three key aspects. Firstly, an attention-based domain adaptation module is introduced to automatically identify and incorporate domain-sensitive features during training. Secondly, we propose a fusion gate module that enables the seamless integration of commonalities and diversities among domains, allowing for implicit characterization of intricate domain relationships. Lastly, we tackle the issue of joint optimization by deriving loss weights from two complementary viewpoints: domain complexity and domain specificity, alleviating inconsistencies among different domains during the training phase. Experiments on three public datasets demonstrate the effectiveness and superiority of our proposed framework. In addition, D3 has been implemented on a real-life, high-traffic internet platform catering to millions of users daily.