Graph neural networks (GNNs) have achieved remarkable success in recommender
systems by representing users and items based on their historical interactions. However,
little attention was paid to GNN's vulnerability to exposure bias: users are exposed to a
limited number of items so that a system only learns a biased view of user preference to
result in suboptimal recommendation quality. Although inverse propensity weighting is
known to recognize and alleviate exposure bias, it usually works on the final objective with …