  Recently, graph neural networks (GNNs) have been successfully applied to
recommender systems. In recommender systems, the user's feedback behavior on an
item is usually the result of multiple factors acting at the same time.
However, a long-standing challenge is how to effectively aggregate multi-order
interactions in GNN. In this paper, we propose a Graph Factorization Machine
(GFM) which utilizes the popular Factorization Machine to aggregate multi-order
interactions from neighborhood for recommendation. Meanwhile, cross-domain
recommendation has emerged as a viable method to solve the data sparsity
problem in recommender systems. However, most existing cross-domain
recommendation methods might fail when confronting the graph-structured data.
In order to tackle the problem, we propose a general cross-domain
recommendation framework which can be applied not only to the proposed GFM, but
also to other GNN models. We conduct experiments on four pairs of datasets to
demonstrate the superior performance of the GFM. Besides, based on general
cross-domain recommendation experiments, we also demonstrate that our
cross-domain framework could not only contribute to the cross-domain
recommendation task with the GFM, but also be universal and expandable for
various existing GNN models.
