Large-scale e-commercial platforms in the real-world usually contain various recommendation scenarios (domains) to meet demands of diverse customer groups. Multi-Domain Recommendation (MDR), which aims to jointly improve recommendations on all domains and easily scales to thousands of domains, has attracted increasing attention from practitioners and researchers. Existing MDR methods usually employ a shared structure and several specific components to respectively leverage reusable features and domain-specific information. However, data distribution differs across domains, making it challenging to develop a general model that can be applied to all circumstances. Additionally, during training, shared parameters often suffer from the domain conflict while specific parameters are inclined to overfitting on data sparsity domains. we first present a scalable MDR platform served in Taobao that enables to provide services for thousands of domains without specialists involved. To address the problems of MDR methods, we propose a novel model agnostic learning framework, namely MAMDR, for the multi-domain recommendation. Specifically, we first propose a Domain Negotiation (DN) strategy to alleviate the conflict between domains. Then, we develop a Domain Regularization (DR) to improve the generalizability of specific parameters by learning from other domains. We integrate these components into a unified framework and present MAMDR, which can be applied to any model structure to perform multi-domain recommendation. Finally, we present a large-scale implementation of MAMDR in the Taobao application and construct various public MDR benchmark datasets which can be used for following studies. Extensive experiments on both benchmark datasets and industry datasets demonstrate the effectiveness and generalizability of MAMDR.

翻译：现实世界中大规模电子商务平台通常包含多种推荐场景（领域），以满足不同客户群体的需求。多领域推荐旨在联合提升所有领域的推荐效果，并易于扩展至数千个领域，已引起从业者和研究者的广泛关注。现有多领域推荐方法通常采用共享结构与若干特定组件，分别利用可复用特征和领域特有信息。然而，不同领域的数据分布存在差异，开发适用于所有场景的通用模型极具挑战性。此外，训练过程中共享参数常受领域冲突影响，而特定参数则易在数据稀疏领域产生过拟合。我们首先提出一个部署于淘宝的可扩展多领域推荐平台，无需专家参与即可为数千个领域提供服务。为解决多领域推荐方法的问题，我们提出一种新颖的模型无关学习框架MAMDR。具体而言，我们首先提出领域协商策略以缓解领域间冲突；随后设计领域正则化方法，通过学习其他领域信息增强特定参数的泛化能力。我们将这些组件集成至统一框架中形成MAMDR，该框架可应用于任意模型结构实现多领域推荐。最后，我们展示MAMDR在淘宝应用中的大规模部署方案，并构建多个可用于后续研究的公开多领域推荐基准数据集。在基准数据集与工业数据集上的大量实验证明，MAMDR具有显著的有效性与泛化能力。