2014 Preprint**Abstract:** The problem of finding the missing values of a matrix given a few of its entries, called matrix completion, has gathered a lot of attention in the recent years. Although the problem under the standard low rank assumption is NP-hard, Candès and Recht showed that it can be exactly relaxed if the number of observed entries is sufficiently large. In this work, we introduce a novel matrix completion model that makes use of proximity information about rows and columns by assuming they form communities. This assumpti…

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“…some geometric structure of the matrix rows and columns. There exist several prior works on geometric matrix completion that exploit geometric information (Berg et al, 2017;Kalofolias et al, 2014;Rao et al, 2015) such as graphs encoding relations between rows and columns. More recent works leverage deep learning on geometric domains (Berg et al, 2017;Monti et al, 2017) to extract relevant information from geo-metric data such as graphs.…”

confidence: 99%

“…some geometric structure of the matrix rows and columns. There exist several prior works on geometric matrix completion that exploit geometric information (Berg et al, 2017;Kalofolias et al, 2014;Rao et al, 2015) such as graphs encoding relations between rows and columns. More recent works leverage deep learning on geometric domains (Berg et al, 2017;Monti et al, 2017) to extract relevant information from geo-metric data such as graphs.…”

confidence: 99%

“…For a fair comparison with (Boyarski et al, 2020), we use graphs taken from the synthetic Netflix dataset. Synthetic Netflix is a small synthetic dataset constructed by (Kalofolias et al, 2014) and (Monti et al, 2017), in which the user and item graphs have strong community structure. Noise Ours Ours-FM SGMC 5 1e-3 2e-3 5e-3 10 4e-3 3e-3 1e-2 20 6e-3 6e-3 1e-2…”

confidence: 99%