Graph Convolutional Matrix Completion

#Graph #Machine Learning #Neural Networks #Graph Neural Networks #Matrix Completion

Our topic for this session is Graph Convolutional Matrix Completion (arXiv:1706.02263).

Abstract

Abstract of Graph Convolutional Matrix Completion (arXiv:1706.02263):

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.

What is Matrix Completion

The following is an example of matrix completion from Wikipedia.

Given a ratings matrix in which each entry $(i,j)$ represents the rating of movie $j$ by customer $i$, if customer $i$ has watched movie $j$ and is otherwise missing, we would like to predict the remaining entries in order to make good recommendations to customers on what to watch next.

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