Shared-account Cross-domain Sequential Recommendation (SCSR) task aims to recommend the next item via leveraging the mixed user behaviors in multiple domains. It is gaining immense research attention as more and more users tend to sign up on different platforms and share accounts with others to access domain-specific services. Existing works on SCSR mainly rely on mining sequential patterns via Recurrent Neural Network (RNN)-based models, which suffer from the following limitations: 1) RNN-based methods overwhelmingly target discovering sequential dependencies in single-user behaviors. They are not expressive enough to capture the relationships among multiple entities in SCSR. 2) All existing methods bridge two domains via knowledge transfer in the latent space, and ignore the explicit cross-domain graph structure. 3) None existing studies consider the time interval information among items, which is essential in the sequential recommendation for characterizing different items and learning discriminative representations for them. In this work, we propose a new graph-based solution, namely TiDA-GCN, to address the above challenges. Specifically, we first link users and items in each domain as a graph. Then, we devise a domain-aware graph convolution network to learn userspecific node representations. To fully account for users' domainspecific preferences on items, two effective attention mechanisms are further developed to selectively guide the message passing process. Moreover, to further enhance item- and account-level representation learning, we incorporate the time interval into the message passing, and design an account-aware self-attention module for learning items' interactive characteristics. Experiments demonstrate the superiority of our proposed method from various aspects.
翻译:共有账户交叉序列建议(SCSR)任务旨在通过利用多个领域的混合用户行为来建议下一个项目。它正在获得广泛的研究关注,因为越来越多的用户倾向于在不同的平台上签名,并与其他用户共享账户以获取特定领域服务。目前SSR的工作主要依靠通过基于神经网络的常规模式来挖掘顺序模式,这些模式受到以下限制:(1) 以RNN为基础的方法绝大多数针对发现单一用户行为中的相继依赖性。这些方法不够明确,不足以反映SSR中多个实体之间的关系。(2) 所有现有方法都通过潜在空间的知识转移将两个领域连接起来,忽视明确的跨域图结构。(3) 现有研究没有考虑项目之间的时间间隔信息,这在顺序建议中对不同项目的特点定性和为他们学习歧视性表述至关重要。在这项工作中,我们提出了一个新的基于图表的解决办法,即TiDA-GCN,以应对上述挑战。我们首先将每个领域的用户和项目从一个图表中连接起来。然后,我们设计一个跨域-认知空间的自我定位模式网络,将一个跨域的自我定位模块到一个用户特定设计账户。 进一步学习特定项目。