  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.
  In this work, we aim to simplify the design of GCN to make it more concise
and appropriate for recommendation. We propose a new model named LightGCN,
including only the most essential component in GCN -- neighborhood aggregation
-- for collaborative filtering. Specifically, LightGCN learns user and item
embeddings by linearly propagating them on the user-item interaction graph, and
uses the weighted sum of the embeddings learned at all layers as the final
embedding. Such simple, linear, and neat model is much easier to implement and
train, exhibiting substantial improvements (about 16.0\% relative improvement
on average) over Neural Graph Collaborative Filtering (NGCF) -- a
state-of-the-art GCN-based recommender model -- under exactly the same
experimental setting. Further analyses are provided towards the rationality of
the simple LightGCN from both analytical and empirical perspectives.
