提出自适应专家选择网络，提升多领域推荐的效率与区分度。
Adaptive Conditional Expert Selection Network for Multi-domain Recommendation
- 通过稀疏门控筛选相关专家，仅激活必要模型部分。
- 利用互信息损失增强专家与领域关联，显著提升领域区分能力。
- 适合大规模工业级推荐系统，兼顾性能与计算效率。
混合专家（Mixture-of-Experts, MOE）因其强大的表达能力已成为多领域推荐（MDR）的主流方法。然而，现有MOE方法通常对每个实例激活所有专家，导致可扩展性差且领域与专家间区分度低。此外，常用领域专用网络进一步加剧了这一问题。为此，我们提出一种新方法CESAA，包含条件专家选择（CES）模块和自适应专家聚合（AEA）模块。具体地，CES将稀疏门控策略与共享专家结合；AEA则引入互信息损失，强化专家与特定领域的关联，显著提升专家间的区分度。结果是，每个实例仅激活共享专家和选定的领域专属专家，平衡了计算效率与模型性能。在公开排序数据集和工业检索数据集上的实验验证了该方法在多领域推荐任务中的有效性。
原文摘要 · Abstract (English)
Mixture-of-Experts (MOE) has recently become the de facto standard in Multi-domain recommendation (MDR) due to its powerful expressive ability. However, such MOE-based method typically employs all experts for each instance, leading to scalability issue and low-discriminability between domains and experts. Furthermore, the design of commonly used domain-specific networks exacerbates the scalability issues. To tackle the problems, We propose a novel method named CESAA consists of Conditional Expert Selection (CES) Module and Adaptive Expert Aggregation (AEA) Module to tackle these challenges. Specifically, CES first combines a sparse gating strategy with domain-shared experts. Then AEA utilizes mutual information loss to strengthen the correlations between experts and specific domains, and significantly improve the distinction between experts. As a result, only domain-shared experts and selected domain-specific experts are activated for each instance, striking a balance between computational efficiency and model performance. Experimental results on both public ranking and industrial retrieval datasets verify the effectiveness of our method in MDR tasks.
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