Browsing by Author "Studer, Christoph E."

Now showing 1 - 1 of 1
  • Thumbnail Image
    Item
    Sparse factor analysis for analysis of user content preferences
    (2017-07-11) Baraniuk, Richard G.; Lan, Andrew S.; Studer, Christoph E.; Waters, Andrew E.; Rice University; United States Patent and Trademark Office
    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.