With the recent success of graph convolutional networks (GCNs), they have
been widely applied for recommendation, and achieved impressive performance
gains. The core of GCNs lies in its message passing mechanism to aggregate
neighborhood information. However, we observed that message passing largely
slows down the convergence of GCNs during training, especially for large-scale
recommender systems, which hinders their wide adoption. LightGCN makes an early
attempt to simplify GCNs for collaborative filtering by omitting feature
transformations and nonlinear activations. In this paper, we take one step
further to propose an ultra-simplified formulation of GCNs (dubbed UltraGCN),
which skips infinite layers of message passing for efficient recommendation.
Instead of explicit message passing, UltraGCN resorts to directly approximate
the limit of infinite-layer graph convolutions via a constraint loss.
Meanwhile, UltraGCN allows for more appropriate edge weight assignments and
flexible adjustment of the relative importances among different types of
relationships. This finally yields a simple yet effective UltraGCN model, which
is easy to implement and efficient to train. Experimental results on four
benchmark datasets show that UltraGCN not only outperforms the state-of-the-art
GCN models but also achieves more than 10x speedup over LightGCN.

本文介绍了基于图卷积网络的推荐算法，提出了一种简化的模型 UltraGCN，通过限制损失函数直接逼近无限层图卷积的极限来替代昂贵的消息传递过程。通过实验表明，UltraGCN 相比现有的 GCN 模型和 LightGCN 有较大提升，并且训练速度有 10 倍以上的加速。