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 stationary structures on 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 a novel multi-graph convolutional neural network that can learn meaningful statistical graph-structured patterns from users and items, and a recurrent neural network that applies a learnable diffusion on the score matrix. Our neural network system is computationally attractive as it requires a constant number of parameters independent of the matrix size. We apply our method on several standard datasets, showing that it outperforms state-of-the-art matrix completion techniques.
Federico Monti (Università della Svizzera italiana)
Michael Bronstein (USI Lugano / Tel Aviv University / Intel)
Michael Bronstein is an associate professor of Informatics at USI Lugano in Switzerland, associate professor of Applied Mathematics at Tel Aviv University in Israel, and a Principal Engineer at the Intel Perceptual Computing. Michael got his Ph.D. with distinction in Computer Science from the Technion in 2007. He has held visiting appointments at Stanford, Harvard, and MIT. He is a Senior Member of the IEEE, alumnus of the Technion Excellence Program and the Academy of Achievement, ACM Distinguished Speaker, and a member of the Young Academy of Europe. His research appeared in the international media such as CNN and was recognized by numerous prestigious awards, including several best paper awards, three ERC grants (Starting Grant 2012, Proof of Concept Grant 2016, and Consolidator Grant 2016), Google Faculty Research Award (2016), Radcliffe Fellowship from the Institute for Advanced Study at Harvard University (2017), and Rudolf Diesel Industrial Fellowship from TU Munich (2017). In 2014, he was invited as a Young Scientist to the World Economic Forum, an honor bestowed on forty world's leading scientists under the age of forty. Michael is the author of over 100 papers in top scientific journals and conferences, and inventor of over 25 granted patents. He has chaired over a dozen of conferences and workshops in his field, and has served as area chair at ECCV 2016 and ICCV 2017 and as associate editor of the Computer Vision and Image Understanding journal. Besides academic work, Michael is actively involved in the industry. He has co-founded and served in leading technical and management positions at several startup companies, including Invision, an Israeli startup developing 3D sensing technology acquired by Intel in 2012.
Xavier Bresson (NTU)
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2019 : Poster Session #2 »
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2017 Tutorial: Geometric Deep Learning on Graphs and Manifolds »
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2016 Poster: Learning shape correspondence with anisotropic convolutional neural networks »
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