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LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation
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.
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Citations
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Metadata
- Authors
- He, X., Deng, K., Wang, X., Li, Y., Zhang, Y., & Wang, M.
- Venue
- Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval
- Publisher
- ACM
- Published
- 2020-07-25
- Pages
- 639-648
- OpenAlex
- W3045200674
- Cited by
- 4,430
- References
- 5
- FWCI
- 311.55
- Type
- conference paper
- Language
- en
- Open access
- closed
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