Abstract: A processor may create a preference vector by generating preference vector entries in fields that correspond to content attributes associated with at least one media program presented via a media device, each entry including a playback duration and a year. The processor may create a media entity matrix of entity vectors for entities associated with production of media programs. For each entity vector, the processor may generate matrix entries in fields that correspond to content attributes associated with at least one media program associated with the entity, each matrix entry including a year and a score, the score weighted by an epoch factor based upon a time span associated with the at least one media program presented via the media device. The processor may determine entity rankings based upon dot products between the preference vector and the entity vectors and provide a recommendation based upon the rankings.

Abstract: A method includes creating a base content set having attributes indicative of a first program. Selecting a second program to form a first recommendation set, determining a first similarity score between the base content set and the first recommendation set, and providing a recommendation based on the similarity score.

Abstract: A processor may create a preference vector by generating preference vector entries in fields that correspond to content attributes associated with at least one media program presented via a media device, each entry including a playback duration and a year. The processor may create a media entity matrix of entity vectors for entities associated with production of media programs. For each entity vector, the processor may generate matrix entries in fields that correspond to content attributes associated with at least one media program associated with the entity, each matrix entry including a year and a score, the score weighted by an epoch factor based upon a time span associated with the at least one media program presented via the media device. The processor may determine entity rankings based upon dot products between the preference vector and the entity vectors and provide a recommendation based upon the rankings.

Abstract: A method includes receiving a request for sports programming recommendation, responsive to the receiving step, determining an upcoming program vector and a weighted historical vector, performing a similarity calculation comparing the upcoming program vector and the weighted historical vector to derive a recommendation score, and generating a recommendation based on the recommendation score.

Abstract: A processor may create a preference vector by generating preference vector entries in fields that correspond to content attributes associated with at least one media program presented via a media device, each entry including a playback duration and a year. The processor may create a media entity matrix of entity vectors for entities associated with production of media programs. For each entity vector, the processor may generate matrix entries in fields that correspond to content attributes associated with at least one media program associated with the entity, each matrix entry including a year and a score, the score weighted by an epoch factor based upon a time span associated with the at least one media program presented via the media device. The processor may determine entity rankings based upon dot products between the preference vector and the entity vectors and provide a recommendation based upon the rankings.

Abstract: A method includes creating a base content set having attributes indicative of a first program. Selecting a second program to form a first recommendation set, determining a first similarity score between the base content set and the first recommendation set, and providing a recommendation based on the similarity score.

Abstract: A method includes receiving a request for sports programming recommendation, responsive to the receiving step, determining an upcoming program vector and a weighted historical vector, performing a similarity calculation comparing the upcoming program vector and the weighted historical vector to derive a recommendation score, and generating a recommendation based on the recommendation score.

Abstract: A processor may create a preference vector by generating preference vector entries in fields that correspond to content attributes associated with at least one media program presented via a media device, each entry including a playback duration and a year. The processor may create a media entity matrix of entity vectors for entities associated with production of media programs. For each entity vector, the processor may generate matrix entries in fields that correspond to content attributes associated with at least one media program associated with the entity, each matrix entry including a year and a score, the score weighted by an epoch factor based upon a time span associated with the at least one media program presented via the media device. The processor may determine entity rankings based upon dot products between the preference vector and the entity vectors and provide a recommendation based upon the rankings.