Multi-domain recommender systems benefit from cross-domain representation learning and positive knowledge transfer. Both can be achieved by introducing a specific modeling of input data (i.e. disjoint history) or trying dedicated training regimes. At the same time, treating domains as separate input sources becomes a limitation as it does not capture the interplay that naturally exists between domains. In this work, we efficiently learn multi-domain representation of sequential users' interactions using graph neural networks. We use temporal intra- and inter-domain interactions as contextual information for our method called MAGRec (short for Multi-domAin Graph-based Recommender). To better capture all relations in a multi-domain setting, we learn two graph-based sequential representations simultaneously: domain-guided for recent user interest, and general for long-term interest. This approach helps to mitigate the negative knowledge transfer problem from multiple domains and improve overall representation. We perform experiments on publicly available datasets in different scenarios where MAGRec consistently outperforms state-of-the-art methods. Furthermore, we provide an ablation study and discuss further extensions of our method.

翻译：多域推荐系统受益于跨域表示学习与正向知识迁移。这两者可通过引入输入数据特定建模（即分离历史记录）或尝试专门训练范式来实现。然而，将各域视为独立输入源存在局限性，因为它无法捕捉域间自然存在的相互作用。本研究利用图神经网络高效学习用户序列交互的多域表示。我们采用时序域内与域间交互作为上下文信息，提出名为MAGRec（多域图基推荐器）的方法。为更好捕捉多域场景中的全部关系，我们同时学习两种基于图的序列表示：域引导的近期用户兴趣表示与通用长期兴趣表示。该方法有助于缓解多域间的负向知识迁移问题，并提升整体表示质量。我们在公开数据集的不同场景下进行实验，结果表明MAGRec始终优于现有最优方法。此外，我们通过消融实验进一步探讨了方法的扩展方向。