Sparse Factor Analysis for Analysis of User Content Preferences

A non-transitory memory medium storing program instructions, wherein the program instructions, when executed by a computer, cause the computer to implement:

• receiving input data that includes response data, wherein the response data includes a set of preference values that have been assigned to content items by content users, wherein the preference values are drawn from a universe of possible values;
• computing output data based on the input data using a first latent factor model, wherein the output data includes at least:
• an association matrix that defines a set of 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;
• displaying a visual representation of at least a subset of the association strengths in the association matrix and/or at least a subset of the extents in the concept-preference matrix.

A system comprising:

• a processor;
• and
• memory storing program instructions, wherein the program instructions, when executed by the processor, cause the processor to implement:
• receiving input data that includes response data, wherein the response data includes a set of preference values that have been assigned to content items by content users, wherein the preference values are drawn from a universe of possible values;
• computing output data based on the input data using a first latent factor model, wherein the output data includes at least:
• an association matrix that defines a set of 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;
• displaying a visual representation of at least a subset of the association strengths in the association matrix and/or at least a subset of the extents in the concept-preference matrix.