# Graph Convolutional Matrix Completion

arXiv: https://arxiv.org/abs/1706.02263 · HTML: https://mlsanity.com/paper/1706.02263/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jun 2017.

## Built on by (most cited first, 2 of 2)

- [Sparsity-aware neural user behavior modeling in online interaction platforms](https://mlsanity.com/paper/2202.13491/) (2022)
- [Modality-Independent Graph Neural Networks with Global Transformers for Multimodal Recommendation](https://mlsanity.com/paper/2412.13994/) (2024)

## Abstract (arXiv)

We consider matrix completion for recommender systems from the point of view of link prediction on graphs. Interaction data such as movie ratings can be represented by a bipartite user-item graph with labeled edges denoting observed ratings. Building on recent progress in deep learning on graph-structured data, we propose a graph auto-encoder framework based on differentiable message passing on the bipartite interaction graph. Our model shows competitive performance on standard collaborative filtering benchmarks. In settings where complimentary feature information or structured data such as a social network is available, our framework outperforms recent state-of-the-art methods.