Machine Learning Systems and Methods for Predicting End-User Consumption of Future Multimedia Transmissions

A method carried out by a computing system having access to a database comprising program records each identifying a television (TV) program and including historical presentation-logistics (PL) features, content-descriptor (CD) features, and historical viewer-rating (VR) metrics, wherein the historical PL features comprise information identifying a content-delivery platform that previously sourced the TV program for end-user viewing and specifying a delivery mode used to deliver the TV program, and a release-schedule drop pattern (RSDP) that was used by the content-delivery platform for viewing availability and/or delivery, wherein the CD features characterize the TV program, and wherein the historical VR metrics comprise, for the historical PL features, statistical quantification of viewing performance of the TV program among one or more audience categories,

• wherein the method comprises:
• for each of the program records, identifying from among the program records a most similar TV program based on a quantitative comparison of the CD features of the program record with those of the other program records, wherein the most similar TV program is different from the program of the program record;
• for each of the program records, creating a synthetic program record comprising the historical PL features from the program record and the CD features of the most similar TV program;
• by applying the program records and the synthetic program records as input and the historical VR metrics of the program records as ground-truths, training a machine-learning (ML) model to predict audience performance metrics of the TV programs of the program records;
• and
• configuring the ML model for predicting audience performance metrics of one or more runtime program records respectively associated with hypothetical TV programs not yet available for viewing and/or not yet transmitted.

A non-transitory computer-readable medium having instructions stored thereon that, when carried out by one or more processors of a computing system comprising a database comprising program records each identifying a television (TV) program and including historical presentation-logistics (PL) features, content-descriptor (CD) features, and historical viewer-rating (VR) metrics, wherein the historical PL features comprise information identifying a content-delivery platform that previously sourced the TV program for end-user viewing and specifying a delivery mode used to deliver the TV program, and a release-schedule drop pattern (RSDP) that was used by the content-delivery platform for viewing availability and/or delivery, wherein the CD features characterize the TV program, and wherein the historical VR metrics comprise, for the historical PL features, statistical quantification of viewing performance of the TV program among one or more audience categories, cause the computing system to carry out operations including:

• for each of the program records, identifying from among the program records a most similar TV program based on a quantitative comparison of the CD features of the program record with those of the other program records, wherein the most similar TV program is different from the program of the program record;
• for each of the program records, creating a synthetic program record comprising the historical PL features from the program record and the CD features of the most similar TV program;
• by applying the program records and the synthetic program records as input and the historical VR metrics of the program records as ground-truths, training a machine-learning (ML) model to predict audience performance metrics of the TV programs of the program records;
• and
• configuring the ML model for predicting audience performance metrics of one or more runtime program records respectively associated with hypothetical TV programs not yet available for viewing and/or not yet transmitted.