Abstract: Cross-Domain Recommendation (CDR) can effectively alleviate the data sparsity problem suffered by the traditional recommendation systems via leveraging additional knowledge from other domains. How to model the interaction information of users and items from the source to target domains is the key issue in CDR. In the current CDR algorithms, the higher-order information that is implied by the user-item interaction graph is ignored. To this end, we propose a new framework called Graph Convolutional Broad Cross-Domain Recommender System (GBCD). Specifically, we extend the traditional bipartite graph of user-item interactions to a (D + 1)-partite graph to model the relationship between users and items in each domain, and then use common users as a bridge between the source and target domains to transfer information. The higher-order relationships between users and items are learned by Graph Convolutional Network (GCN) to aggregate neighbor information. However, GCN converges very slowly with a large number of nodes and tends to absorb unreliable interaction noise, resulting in poor robustness. Therefore, we feed the domain-aggregated features to Broad Learning System (BLS), which enhances the robustness of GCN by exploiting the stochastic mapping features of BLS, achieving superior recommendation performance. Experiments conducted on two real datasets show that GBCD outperforms the existing state-of-the-art cross-domain recommendation algorithms.