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:高级推荐人系统通常涉及多种营销战略的多个领域(例如情景或类别),用户与这些系统互动以满足不同的需求。多域建议的目标是同时改善所有域的建议性性能。基于常规图形神经网络的方法通常分别处理每个域,或训练一个共同的模式为所有域服务。前者未能利用用户的跨域行为,使行为分散问题成为一个巨大的障碍。后者学习了所有域的共享用户代表性,忽视了用户的域性偏好。在本文件中,我们提议为MDR建立一个基于相关偏好的等级高频网络框架。MDR代表多域用户-项目互动,形成一个统一的图表,帮助所有域的转移。$\mathf{H3Transer} 包含两个基于高端的模块,即动态项目转移(Hyper-I)和适应性用户汇总(Hyper-U),超I从多域用户-项目的反馈中提取了相关信息,用于消除高域域端用户-高端数据级数据库的升级。 高端用户-透明级数据库只改进了多域域域域的系统(AS-RO-RO-RO-RO-RO-RO-RO-RO-I)的升级用户对数据库的演示。