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Shared-account Cross-domain Sequential recommendation (SCSR) is the task ofrecommending the next item based on a sequence of recorded user behaviors,where multiple users share a single account, and their behaviours are availablein multiple domains. Existing work on solving SCSR mainly relies on miningsequential patterns via RNN-based models, which are not expressive enough tocapture the relationships among multiple entities. Moreover, all existingalgorithms try to bridge two domains via knowledge transfer in the latentspace, and the explicit cross-domain graph structure is unexploited. In thiswork, we propose a novel graph-based solution, namely DA-GCN, to address theabove challenges. Specifically, we first link users and items in each domain asa graph. Then, we devise a domain-aware graph convolution network to learnuser-specific node representations. To fully account for users' domain-specificpreferences on items, two novel attention mechanisms are further developed toselectively guide the message passing process. Extensive experiments on tworeal-world datasets are conducted to demonstrate the superiority of our DA-GCNmethod.

DA-GCN: A Domain-aware Attentive Graph Convolution Network for Shared-account Cross-domain Sequential Recommendation