Shared-account Cross-domain Sequential recommendation (SCSR) is the task of
recommending the next item based on a sequence of recorded user behaviors,
where multiple users share a single account, and their behaviours are available
in multiple domains. Existing work on solving SCSR mainly relies on mining
sequential patterns via RNN-based models, which are not expressive enough to
capture the relationships among multiple entities. Moreover, all existing
algorithms try to bridge two domains via knowledge transfer in the latent
space, and the explicit cross-domain graph structure is unexploited. In this
work, we propose a novel graph-based solution, namely DA-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
user-specific node representations. To fully account for users' domain-specific
preferences on items, two novel attention mechanisms are further developed to
selectively guide the message passing process. Extensive experiments on two
real-world datasets are conducted to demonstrate the superiority of our DA-GCN
method.

本论文提出了一种基于图的推荐算法 DA-GCN，通过关注用户特定节点表示和选定注意机制来解决交叉领域序列推荐任务中的挑战。