Determining recommended media programming from sparse consumption data

A non-transitory computer-readable medium storing a plurality of instructions which, when executed by a processor, cause the processor to perform operations, the operations comprising:

• receiving a data set identifying at least one media program presented via a media device;
• creating a preference vector comprising a plurality of fields corresponding to a plurality of content attributes, wherein the creating the preference vector comprises generating preference vector entries in fields of the plurality of fields that correspond to content attributes associated with the at least one media program presented via the media device, wherein each of the preference vector entries comprises a playback duration of one of the at least one media program presented via the media device and a time period of the one of the at least one media program presented via the media device;
• creating a media entity matrix comprising entity vectors for a plurality of entities associated with a production of a plurality of media programs, wherein each entity vector comprises the plurality of fields corresponding to the plurality of content attributes, wherein the creating the media entity matrix comprises, for each entity vector for each entity of the plurality of entities, generating matrix entries in fields of the plurality of fields that correspond to content attributes associated with at least one media program associated with the each entity, wherein each matrix entry comprises a time period of one of the at least one media program associated with the each entity and a score of the one of the at least one media program associated with the each entity, wherein the score is weighted by an epoch factor that is based upon a time span that is associated with the at least one media program presented via the media device;
• determining entity rankings based upon dot products between the preference vector and the entity vectors of the media entity matrix;
• and
• providing a recommendation based upon the entity rankings.

A method comprising:

• receiving, by a processor, a data set identifying at least one media program presented via a media device;
• creating, by the processor, a preference vector comprising a plurality of fields corresponding to a plurality of content attributes, wherein the creating the preference vector comprises generating preference vector entries in fields of the plurality of fields that correspond to content attributes associated with the at least one media program presented via the media device, wherein each of the preference vector entries comprises a playback duration of one of the at least one media program presented via the media device and a time period of the one of the at least one media program presented via the media device;
• creating, by the processor, a media entity matrix comprising entity vectors for a plurality of entities associated with a production of a plurality of media programs, wherein each entity vector comprises the plurality of fields corresponding to the plurality of content attributes, wherein the creating the media entity matrix comprises, for each entity vector for each entity of the plurality of entities, generating matrix entries in fields of the plurality of fields that correspond to content attributes associated with at least one media program associated with the each entity, wherein each matrix entry comprises a time period of one of the at least one media program associated with the each entity and a score of the one of the at least one media program associated with the each entity, wherein the score is weighted by an epoch factor that is based upon a time span that is associated with the at least one media program presented via the media device;
• determining, by the processor, entity rankings based upon dot products between the preference vector and the entity vectors of the media entity matrix;
• and
• providing, by the processor, a recommendation based upon the entity rankings.