Sparse factor analysis for analysis of user content preferences

Abstract

A mechanism for discerning user preferences for categories of provided content. A computer receives response data including a set of preference values that have been assigned to content items by content users. Output data is computed based on the response data using a latent factor model. The output data includes at least: an association matrix that defines K concepts associated with the content items, wherein K is smaller than the number of the content items, wherein, for each of the K concepts, the association matrix defines the concept by specifying strengths of association between the concept and the content items; and a concept-preference matrix including, for each content user and each of the K concepts, an extent to which the content user prefers the concept. The computer may display a visual representation of the association strengths in the association matrix and/or the extents in the concept-preference matrix.

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Baraniuk, Richard G., Lan, Andrew S., Studer, Christoph E. and Waters, Andrew E., "Sparse factor analysis for analysis of user content preferences." Patent US9704102B2. issued 2017-07-11. Retrieved from https://hdl.handle.net/1911/96656.

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