Graph Convolution Networks (GCNs) manifest great potential in recommendation.
This is attributed to their capability on learning good user and item
embeddings by exploiting the collaborative signals from the high-order
neighbors. Like other GCN models, the GCN based recommendation models also
suffer from the notorious over-smoothing problem - when stacking more layers,
node embeddings become more similar and eventually indistinguishable, resulted
in performance degradation. The recently proposed LightGCN and LR-GCN alleviate
this problem to some extent, however, we argue that they overlook an important
factor for the over-smoothing problem in recommendation, that is, high-order
neighboring users with no common interests of a user can be also involved in
the user's embedding learning in the graph convolution operation. As a result,
the multi-layer graph convolution will make users with dissimilar interests
have similar embeddings. In this paper, we propose a novel Interest-aware
Message-Passing GCN (IMP-GCN) recommendation model, which performs high-order
graph convolution inside subgraphs. The subgraph consists of users with similar
interests and their interacted items. To form the subgraphs, we design an
unsupervised subgraph generation module, which can effectively identify users
with common interests by exploiting both user feature and graph structure. To
this end, our model can avoid propagating negative information from high-order
neighbors into embedding learning. Experimental results on three large-scale
benchmark datasets show that our model can gain performance improvement by
stacking more layers and outperform the state-of-the-art GCN-based
recommendation models significantly.

该研究论文提出了一种新颖的基于 Interest-aware Message-Passing GCN 的推荐模型，通过在用户兴趣相似的子图中进行高阶图卷积操作，避免了过度平滑问题，其在三个基准数据集上的实验结果表明，该模型可以通过叠加更多的层次获得性能提升，并且在 GCN-based 推荐模型上显著优于现有的最佳模型。