Graph Convolutional Matrix Completion

Rianne van den Berg, Thomas N. Kipf, Max Welling

6/7/2017

stat.MLcs.DBcs.IR

Official code: riannevdberg/gc-mc (414 stars, last commit 2018-11-15).

Abstract

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 Implementations(1)

riannevdberg/gc-mcOfficial100%

414101Shell, PythonJan 26, 20187 years ago

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Cite this paper

@article{berg2017graph,
  title  = {Graph Convolutional Matrix Completion},
  author = {Rianne van den Berg and Thomas N. Kipf and Max Welling},
  year   = {2017},
  eprint = {1706.02263},
  archivePrefix = {arXiv},
  url    = {https://arxiv.org/abs/1706.02263}
}

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