mgcnn
Multi-Graph Convolutional Neural Networks
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Matrix completion models are among the most common formulations of recommender systems. Recent works have showed a boost of performance of these techniques when introducing the pairwise relationships between users/items in the form of graphs, and imposing smoothness priors on these graphs. However, such techniques do not fully exploit the local stationarity structures of user/item graphs, and the number of parameters to learn is linear w.r.t. the number of users and items. We propose a novel approach to overcome these limitations by using geometric deep learning on graphs. Our matrix completion architecture combines graph convolutional neural networks and recurrent neural networks to learn meaningful statistical graph-structured patterns and the non-linear diffusion process that generates the known ratings. This neural network system requires a constant number of parameters independent of the matrix size. We apply our method on both synthetic and real datasets, showing that it outperforms state-of-the-art techniques.
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Geometric matrix completion (GMC) has been proposed for recommendation b...
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We consider a discrete-valued matrix completion problem for recommender
...
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Recommender systems (RS), which have been an essential part in a wide ra...
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We consider matrix completion for recommender systems from the point of ...
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The goal of a recommendation system is to predict the interest of a user...
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The problem of completing high-dimensional matrices from a limited set o...
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Recommender systems can be formulated as a matrix completion problem,
pr...
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