  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.
