UltraGCN: Ultra Simplification of Graph Convolutional Networks for Recommendation
By Kelong Mao, Jieming Zhu, Xi Xiao, Biao Lu, Zhaowei Wang, Xiuqiang He. International Conference on Information and Knowledge Management, 2021
471 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence, Computer Networks and Communications, Information Systems
View Paper: https://www.semanticscholar.org/paper/17be6098c6dba71eef609b3d45cf63a073c94221
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Citations in the corpus
- A Survey of Graph Neural Networks for Social Recommender Systems (2022, background): “It is well-known that non-linear activation and feature transformation in GCN encoders make the propagation step very complicated for training and scalability [25, 60].”