  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 the model outputs,
whereas GNN can also be biased during neighbor aggregation. In this paper, we
propose a simple but effective approach, neighbor aggregation via inverse
propensity (Navip) for GNNs. Specifically, given a user-item bipartite graph,
we first derive propensity score of each user-item interaction in the graph.
Then, inverse of the propensity score with Laplacian normalization is applied
to debias neighbor aggregation from exposure bias. We validate the
effectiveness of our approach through our extensive experiments on two public
and Amazon Alexa datasets where the performance enhances up to 14.2%.
