Validating a target audience using a combination of classification algorithms

A non-transitory computer readable storage medium storing instructions thereon that, when executed by at least one processor, cause a computing device to:

• receive demographic-label statistics associated with digital content items;
• access session-level data associated with the digital content items, wherein the session-level data comprises session-level features for sessions in which a user of a plurality of users consumed one or more of the digital content items;
• utilize a first classification algorithm to determine a user-level-latent feature for the user by analyzing subsets of the demographic-label statistics particular to different digital content items consumed by the user and a listing of the different digital content items consumed by the user;
• generate a first coefficient learned by a supervised learning model for user-level-latent features processed by the supervised learning model;
• generate a second coefficient learned by the supervised learning model for session-level features processed by the supervised learning model;
• and
• utilize a second classification algorithm to determine a demographic label for the user by analyzing the user-level-latent feature for the user, the session-level features, the first coefficient learned by the supervised learning model, and the second coefficient learned by the supervised learning model.

A system for determining demographic labels using digital inputs within a predictive model for demographic classification comprising:

• memory comprising demographic-label statistics for digital content items;
• and
• a server computing device comprising instructions thereon that, when executed by the server computing device, cause the system to:
• access the demographic-label statistics for the digital content items;
• access session-level data concerning the digital content items, wherein the session-level data comprises session-level features for sessions in which a user of a plurality of users consumed one or more of the digital content items;
• utilize a first classification algorithm to determine a prior probability of a possible demographic label for the user by identifying, from within the demographic-label statistics and for each digital content item consumed by the user, a collective probability of the possible demographic label for consumers of each digital content item consumed by the user and determining a product of each collective probability of the possible demographic label;
• generate a first coefficient learned by a supervised learning model for prior probabilities of possible demographic labels processed by the supervised learning model;
• generate a second coefficient learned by the supervised learning model for session-level features processed by the supervised learning model;
• and
• utilize a second classification algorithm to determine a statistical probability of a demographic label for the user by analyzing a number of sessions for the user, the prior probability of the possible demographic label for the user, the session-level features, the first coefficient learned by the supervised learning model, and the second coefficient learned by the supervised learning model.