  Social recommendation aims to fuse social links with user-item interactions
to alleviate the cold-start problem for rating prediction. Recent developments
of Graph Neural Networks (GNNs) motivate endeavors to design GNN-based social
recommendation frameworks to aggregate both social and user-item interaction
information simultaneously. However, most existing methods neglect the social
inconsistency problem, which intuitively suggests that social links are not
necessarily consistent with the rating prediction process. Social inconsistency
can be observed from both context-level and relation-level. Therefore, we
intend to empower the GNN model with the ability to tackle the social
inconsistency problem. We propose to sample consistent neighbors by relating
sampling probability with consistency scores between neighbors. Besides, we
employ the relation attention mechanism to assign consistent relations with
high importance factors for aggregation. Experiments on two real-world datasets
verify the model effectiveness.
