  Graph neural networks (GNNs) have shown impressive performance in recommender
systems, particularly in collaborative filtering (CF). The key lies in
aggregating neighborhood information on a user-item interaction graph to
enhance user/item representations. However, we have discovered that this
aggregation mechanism comes with a drawback, which amplifies biases present in
the interaction graph. For instance, a user's interactions with items can be
driven by both unbiased true interest and various biased factors like item
popularity or exposure. However, the current aggregation approach combines all
information, both biased and unbiased, leading to biased representation
learning. Consequently, graph-based recommenders can learn distorted views of
users/items, hindering the modeling of their true preferences and
generalizations. To address this issue, we introduce a novel framework called
Adversarial Graph Dropout (AdvDrop). It differentiates between unbiased and
biased interactions, enabling unbiased representation learning. For each
user/item, AdvDrop employs adversarial learning to split the neighborhood into
two views: one with bias-mitigated interactions and the other with bias-aware
interactions. After view-specific aggregation, AdvDrop ensures that the
bias-mitigated and bias-aware representations remain invariant, shielding them
from the influence of bias. We validate AdvDrop's effectiveness on five public
datasets that cover both general and specific biases, demonstrating significant
improvements. Furthermore, our method exhibits meaningful separation of
subgraphs and achieves unbiased representations for graph-based CF models, as
revealed by in-depth analysis. Our code is publicly available at
https://github.com/Arthurma71/AdvDrop.
