LightGCN

2020/07/25 by Xiangnan He, Kuan Deng, Xiang Wang +3 · 354 citations
Computer Science · #Advanced Graph Neural Networks #Image and Video Quality Assessment #Recommender Systems and Techniques

paper · doi:10.1145/3397271.3401063

openalex publication_date 2020/07/25 · openalex created_date 2020/07/29 · openalex updated_date 2026/07/31

Abstract

Graph Convolution Network (GCN) has become new state-of-the-art for collaborative filtering. Nevertheless, the reasons of its effectiveness for recommendation are not well understood. Existing work that adapts GCN to recommendation lacks thorough ablation analyses on GCN, which is originally designed for graph classification tasks and equipped with many neural network operations. However, we empirically find that the two most common designs in GCNs -- feature transformation and nonlinear activation -- contribute little to the performance of collaborative filtering. Even worse, including them adds to the difficulty of training and degrades recommendation performance.

Citations

• Matrix Factorization Techniques for Recommender Systems
• Temporal Relational Ranking for Stock Prediction

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