Low-rank matrix factorization with attributes

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dc.creatorAbernethy, Jacob
dc.creatorBach, Francis
dc.creatorEvgeniou, Theodoros
dc.creatorVert, Jean-Philippe
dc.date2006-11-24
dc.date.accessioned2026-07-07T07:31:46Z
dc.date.available2026-07-07T07:31:46Z
dc.descriptionWe develop a new collaborative filtering (CF) method that combines both previously known users' preferences, i.e. standard CF, as well as product/user attributes, i.e. classical function approximation, to predict a given user's interest in a particular product. Our method is a generalized low rank matrix completion problem, where we learn a function whose inputs are pairs of vectors -- the standard low rank matrix completion problem being a special case where the inputs to the function are the row and column indices of the matrix. We solve this generalized matrix completion problem using tensor product kernels for which we also formally generalize standard kernel properties. Benchmark experiments on movie ratings show the advantages of our generalized matrix completion method over the standard matrix completion one with no information about movies or people, as well as over standard multi-task or single task learning methods.
dc.description12 pages, 2 figures
dc.identifierhttps://arxiv.org/abs/cs/0611124
dc.identifierhttp://arxiv.org/abs/cs/0611124
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/118871
dc.subjectMachine Learning
dc.subjectArtificial Intelligence
dc.subjectInformation Retrieval
dc.titleLow-rank matrix factorization with attributes
dc.typetext

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