  Recently, Graph Convolutional Network (GCN) has become a novel state-of-art
for Collaborative Filtering (CF) based Recommender Systems (RS). It is a common
practice to learn informative user and item representations by performing
embedding propagation on a user-item bipartite graph, and then provide the
users with personalized item suggestions based on the representations. Despite
effectiveness, existing algorithms neglect precious interactive features
between user-item pairs in the embedding process. When predicting a user's
preference for different items, they still aggregate the user tree in the same
way, without emphasizing target-related information in the user neighborhood.
Such a uniform aggregation scheme easily leads to suboptimal user and item
representations, limiting the model expressiveness to some extent.
  In this work, we address this problem by building bilateral interactive
guidance between each user-item pair and proposing a new model named IA-GCN
(short for InterActive GCN). Specifically, when learning the user
representation from its neighborhood, we assign higher attention weights to
those neighbors similar to the target item. Correspondingly, when learning the
item representation, we pay more attention to those neighbors resembling the
target user. This leads to interactive and interpretable features, effectively
distilling target-specific information through each graph convolutional
operation. Our model is built on top of LightGCN, a state-of-the-art GCN model
for CF, and can be combined with various GCN-based CF architectures in an
end-to-end fashion. Extensive experiments on three benchmark datasets
demonstrate the effectiveness and robustness of IA-GCN.
