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2010
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One-Class Matrix Completion with Low-Density Factorizations

11 years 3 months ago
One-Class Matrix Completion with Low-Density Factorizations
Consider a typical recommendation problem. A company has historical records of products sold to a large customer base. These records may be compactly represented as a sparse customer-times-product "who-bought-what" binary matrix. Given this matrix, the goal is to build a model that provides recommendations for which products should be sold next to the existing customer base. Such problems may naturally be formulated as collaborative filtering tasks. However, this is a one-class setting, that is, the only known entries in the matrix are one-valued. If a customer has not bought a product yet, it does not imply that the customer has a low propensity to potentially be interested in that product. In the absence of entries explicitly labeled as negative examples, one may resort to considering unobserved customer-product pairs as either missing data or as surrogate negative instances. In this paper, we propose an approach to explicitly deal with this kind of ambiguity by instead tre...
Vikas Sindhwani, Serhat Selcuk Bucak, Jianying Hu,
Added 12 Feb 2011
Updated 12 Feb 2011
Type Journal
Year 2010
Where ICDM
Authors Vikas Sindhwani, Serhat Selcuk Bucak, Jianying Hu, Aleksandra Mojsilovic
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