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...

• Type: Journal
• Year: 2010
• Where: ICDM
• Authors: Vikas Sindhwani, Serhat Selcuk Bucak, Jianying Hu, Aleksandra Mojsilovic
• Keywords: Collaborative Filtering Tasks, Customer Base, Data Mining, ICDM 2010, Matrix