Graph Convolutional Matrix Completion
This work addresses recommendation accuracy for users and businesses, offering an incremental improvement by integrating graph-based deep learning into collaborative filtering.
The paper tackles matrix completion for recommender systems by framing it as link prediction on bipartite graphs, proposing a graph auto-encoder based on differentiable message passing. The model achieves competitive performance on standard benchmarks and outperforms state-of-the-art methods when additional feature or social network data is available.
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.