Graph Convolutional Matrix Completion

7 Jun 2017  ·  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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Results from the Paper

Ranked #4 on Recommendation Systems on YahooMusic Monti (using extra training data)

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Task Dataset Model Metric Name Metric Value Global Rank Uses Extra
Training Data
Result Benchmark
Recommendation Systems Douban Monti GC-MC RMSE 0.734 # 5
Recommendation Systems Flixster Monti GC-MC RMSE 0.917 # 5
Recommendation Systems MovieLens 100K GC-MC + feat RMSE (u1 Splits) 0.905 # 6
Recommendation Systems MovieLens 100K GC-MC RMSE (u1 Splits) 0.910 # 8
Recommendation Systems MovieLens 10M GC-MC RMSE 0.777 # 9
Recommendation Systems MovieLens 1M GC-MC RMSE 0.832 # 6
Recommendation Systems YahooMusic Monti GC-MC RMSE 20.5 # 4


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