Papers › Graph Convolutional Matrix Completion
Graph Convolutional Matrix Completion
Rianne van den Berg, Thomas N. Kipf, Max Welling
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.
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Code
17 repositories listed; official and paper-mentioned ones first.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Recommendation Systems | Douban Monti | GC-MC | RMSE | 0.734 | #5 of 8 | Archive leaderboard | report |
| Recommendation Systems | Flixster Monti | GC-MC | RMSE | 0.917 | #5 of 7 | Archive leaderboard | report |
| Recommendation Systems | MovieLens 100K | GC-MC | RMSE (u1 Splits) | 0.905 | #8 of 18 | Archive leaderboard | report |
| Recommendation Systems | MovieLens 100K | GC-MC | RMSE (u1 Splits) | 0.910 | #9 of 18 | Archive leaderboard | report |
| Recommendation Systems | MovieLens 10M | GC-MC | RMSE | 0.777 | #9 of 17 | Archive leaderboard | report |
| Recommendation Systems | MovieLens 1M | GC-MC | RMSE | 0.832 | #6 of 31 | Archive leaderboard | report |
| Recommendation Systems | YahooMusic Monti | GC-MC | RMSE | 20.5 | #4 of 6 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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