Graph Convolutional Networks (GCNs) are state-of-the-art graph based
representation learning models by iteratively stacking multiple layers of
convolution aggregation operations and non-linear activation operations.
Recently, in Collaborative Filtering (CF) based Recommender Systems (RS), by
treating the user-item interaction behavior as a bipartite graph, some
researchers model higher-layer collaborative signals with GCNs. These GCN based
recommender models show superior performance compared to traditional works.
However, these models suffer from training difficulty with non-linear
activations for large user-item graphs. Besides, most GCN based models could
not model deeper layers due to the over smoothing effect with the graph
convolution operation. In this paper, we revisit GCN based CF models from two
aspects. First, we empirically show that removing non-linearities would enhance
recommendation performance, which is consistent with the theories in simple
graph convolutional networks. Second, we propose a residual network structure
that is specifically designed for CF with user-item interaction modeling, which
alleviates the over smoothing problem in graph convolution aggregation
operation with sparse user-item interaction data. The proposed model is a
linear model and it is easy to train, scale to large datasets, and yield better
efficiency and effectiveness on two real datasets. We publish the source code
at this https URL

本文提出了一种无非线性激活函数的简化 GCN 结构，以提高推荐性能，并提出了一种专门用于 CF 的残差网络结构来解决大规模数据下图卷积聚合操作的平滑问题，该模型易于训练、扩展到大规模数据集，并在两个真实数据集上展示出更好的效率和效果。