Multi-domain recommendation (MDR) aims to enhance recommendation performance across various domains. However, real-world recommender systems in online platforms often need to handle dozens or even hundreds of domains, far exceeding the capabilities of traditional MDR algorithms, which typically focus on fewer than five domains. Key challenges include a substantial increase in parameter count, high maintenance costs, and intricate knowledge transfer patterns across domains. Furthermore, minor domains often suffer from data sparsity, leading to inadequate training in classical methods. To address these issues, we propose Adaptive REcommendation for All Domains with counterfactual augmentation (AREAD). AREAD employs a hierarchical structure with a limited number of expert networks at several layers, to effectively capture domain knowledge at different granularities. To adaptively capture the knowledge transfer pattern across domains, we generate and iteratively prune a hierarchical expert network selection mask for each domain during training. Additionally, counterfactual assumptions are used to augment data in minor domains, supporting their iterative mask pruning. Our experiments on two public datasets, each encompassing over twenty domains, demonstrate AREAD's effectiveness, especially in data-sparse domains. Source code is available at https://github.com/Chrissie-Law/AREAD-Multi-Domain-Recommendation.

翻译：多领域推荐旨在提升跨多个领域的推荐性能。然而，在线平台中的实际推荐系统通常需要处理数十甚至数百个领域，这远远超出了传统多领域推荐算法的能力范围（后者通常专注于少于五个领域）。关键挑战包括参数量的大幅增加、高昂的维护成本以及跨领域复杂的知识迁移模式。此外，小领域常受数据稀疏性问题困扰，导致经典方法训练不足。为解决这些问题，我们提出了基于反事实增强的全领域自适应推荐方法。该方法采用分层结构，在若干层级上部署有限数量的专家网络，以有效捕获不同粒度的领域知识。为自适应地捕获跨领域的知识迁移模式，我们在训练过程中为每个领域生成并迭代剪枝一个分层专家网络选择掩码。此外，我们利用反事实假设来增强小领域的数据，以支持其迭代掩码剪枝过程。我们在两个各涵盖超过二十个领域的公开数据集上进行的实验证明了该方法的有效性，尤其是在数据稀疏的领域。源代码发布于 https://github.com/Chrissie-Law/AREAD-Multi-Domain-Recommendation。