Advanced recommender systems usually involve multiple domains (such as scenarios or categories) for various marketing strategies, and users interact with them to satisfy diverse demands. The goal of multi-domain recommendation (MDR) is to improve the recommendation performance of all domains simultaneously. Conventional graph neural network based methods usually deal with each domain separately, or train a shared model to serve all domains. The former fails to leverage users' cross-domain behaviors, making the behavior sparseness issue a great obstacle. The latter learns shared user representation with respect to all domains, which neglects users' domain-specific preferences. In this paper we propose $\mathsf{H^3Trans}$, a hierarchical hypergraph network based correlative preference transfer framework for MDR, which represents multi-domain user-item interactions into a unified graph to help preference transfer. $\mathsf{H^3Trans}$ incorporates two hyperedge-based modules, namely dynamic item transfer (Hyper-I) and adaptive user aggregation (Hyper-U). Hyper-I extracts correlative information from multi-domain user-item feedbacks for eliminating domain discrepancy of item representations. Hyper-U aggregates users' scattered preferences in multiple domains and further exploits the high-order (not only pair-wise) connections to improve user representations. Experiments on both public and production datasets verify the superiority of $\mathsf{H^3Trans}$ for MDR.

翻译：先进的推荐系统通常涉及多个域（如场景或类别）以实施多样化营销策略，用户通过与这些域的交互来满足不同需求。多域推荐的目标是同时提升所有域的推荐性能。传统基于图神经网络的方法通常独立处理每个域，或训练共享模型服务于所有域。前者无法利用用户的跨域行为，导致行为稀疏性问题成为重大障碍；后者学习面向所有域的共享用户表征，却忽略了用户对特定域的偏好。本文提出$\mathsf{H^3Trans}$——一种基于层级超图网络的关联偏好迁移框架用于多域推荐，该框架将多域用户-物品交互表示为统一图结构以促进偏好迁移。$\mathsf{H^3Trans}$包含两个基于超边的模块：动态物品迁移模块和自适应用户聚合模块。前者从多域用户-物品反馈中提取关联信息以消除物品表征的域差异；后者聚合用户在多个域中的分散偏好，并进一步利用高阶（非仅成对）连接关系优化用户表征。在公开数据集与生产数据集上的实验验证了$\mathsf{H^3Trans}$在多域推荐中的优越性。