用因果分析优化多领域推荐分组，提升跨域效果
CDC: Causal Domain Clustering for Multi-Domain Recommendation
- 基于因果发现构建双效关联矩阵，捕捉领域间静态与动态交互
- 在50+领域上实现4.9% eCPM提升，工业场景表现显著
- 适合大规模多领域推荐系统设计者与算法工程师
多领域推荐利用领域通用知识提升跨领域推荐效果。但当平台扩展至数十甚至上百场景时，统一训练所有领域会导致性能下降，因领域间差异显著。现有基于业务逻辑或数据相似性的分组方法难以捕捉最优迁移关系。为此，提出因果域聚类（CDC）。CDC通过两种效应建模：孤立域亲和矩阵用于刻画非交互领域间的转移模式，混合域亲和矩阵则考虑联合训练下的动态协同或干扰。引入因果发现计算基于凝聚力的系数，自适应平衡两者贡献。共优化动态聚类算法迭代优化目标域分组与源域选择。在公开数据集及工业场景中，对超过50个领域验证，显著提升性能，线上eCPM提升4.9%。代码已开源。
原文摘要 · Abstract (English)
Multi-domain recommendation leverages domain-general knowledge to improve recommendations across several domains. However, as platforms expand to dozens or hundreds of scenarios, training all domains in a unified model leads to performance degradation due to significant inter-domain differences. Existing domain grouping methods, based on business logic or data similarities, often fail to capture the true transfer relationships required for optimal grouping. To effectively cluster domains, we propose Causal Domain Clustering (CDC). CDC models domain transfer patterns within a large number of domains using two distinct effects: the Isolated Domain Affinity Matrix for modeling non-interactive domain transfers, and the Hybrid Domain Affinity Matrix for considering dynamic domain synergy or interference under joint training. To integrate these two transfer effects, we introduce causal discovery to calculate a cohesion-based coefficient that adaptively balances their contributions. A Co-Optimized Dynamic Clustering algorithm iteratively optimizes target domain clustering and source domain selection for training. CDC significantly enhances performance across over 50 domains on public datasets and in industrial settings, achieving a 4.9% increase in online eCPM. Code is available at https://github.com/Chrissie-Law/Causal-Domain-Clustering-for-Multi-Domain-Recommendation
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