  Graph Convolutional Network (GCN) has achieved great success and has been
applied in various fields including recommender systems. However, GCN still
suffers from many issues such as training difficulties, over-smoothing,
vulnerable to adversarial attacks, etc. Distinct from current GCN-based methods
which simply employ GCN for recommendation, in this paper we are committed to
build a robust GCN model for collaborative filtering. Firstly, we argue that
recursively incorporating messages from different order neighborhood mixes
distinct node messages indistinguishably, which increases the training
difficulty; instead we choose to separately aggregate different order neighbor
messages with a simple GCN model which has been shown effective; then we
accumulate them together in a hierarchical way without introducing additional
model parameters. Secondly, we propose a solution to alleviate over-smoothing
by randomly dropping out neighbor messages at each layer, which also well
prevents over-fitting and enhances the robustness. Extensive experiments on
three real-world datasets demonstrate the effectiveness and robustness of our
model.
