Content recommendations based upon historical future data

A computing device comprising:

• a processor;
• and
• memory comprising processor-executable instructions that when executed by the processor cause performance of operations, the operations comprising:
• computing, by a recommendation system hosted by one or more servers, a model serving delay time as an average of training delays of events, wherein a training delay corresponds to a difference between an event serve time of an event and a train time of a model that served a recommendation associated with the event;
• in response to determining that a current time period is a first type of time period, determining, by the recommendation system hosted by the one or more servers, a historical data time interval as a second time period prior to the current time period plus the model serving delay time;
• using the historical data time interval determined by the recommendation system hosted by the one or more servers to identify historic user distribution data and historic content distribution data associated with the historical data time interval;
• training, by the recommendation system hosted by the one or more servers, the model for predicting user content preferences using the historic user distribution data and the historic content distribution data associated with the historical data time interval;
• utilizing, by the recommendation system hosted by the one or more servers, the model to generate a content recommendation for a user;
• providing, by the recommendation system hosted by the one or more servers, the content recommendation for display on a client device of the user;
• in response to detecting a first event corresponding to the user interacting with the content recommendation, generating, by the recommendation system hosted by the one or more servers, an entry indicating at least one of an event type of the first event, a first serve time of the model serving the content recommendation, or a first train time of the model being trained using at least one of the historic user distribution data and the historic content distribution data;
• providing, by the recommendation system hosted by the one or more servers, second content recommendation for display on the client device of the user;
• and
• in response to detecting a second event associated with the second content recommendation, generating, by the recommendation system hosted by the one or more servers, a second entry indicating a second event type of the second event, a second serve time of the model serving the second content recommendation, and a second train time of the model being trained using the historic user distribution data and the historic content distribution data, wherein the second event corresponds to an impression of the user viewing the second content recommendation.

A non-transitory machine readable medium having stored thereon processor-executable instructions that when executed cause performance of operations, the operations comprising:

• computing, by a recommendation system hosted by one or more servers, a model serving delay time as an average of training delays of events, wherein a training delay corresponds to a difference between an event serve time of an event and a train time of a model that served a recommendation associated with the event;
• in response to determining that a current time period is a first type of time period that does not occur within a set of time periods, determining, by the recommendation system hosted by the one or more servers, a historical data time interval as a plurality of time periods prior to the current time period plus the model serving delay time;
• using the historical data time interval determined by the recommendation system hosted by the one or more servers to identify historic user distribution data and historic content distribution data associated with the historical data time interval;
• training, by the recommendation system hosted by the one or more servers, the model for predicting user content preferences using the historic user distribution data and the historic content distribution data associated with the historical data time interval;
• utilizing, by the recommendation system hosted by the one or more servers, the model to generate a content recommendation for a user;
• providing, by the recommendation system hosted by the one or more servers, the content recommendation for display on a client device of the user;
• in response to detecting a first event corresponding to the user interacting with the content recommendation, generating, by the recommendation system hosted by the one or more servers, an entry indicating at least one of an event type of the first event, a first serve time of the model serving the content recommendation, or a first train time of the model being trained using at least one of the historic user distribution data or the historic content distribution data;
• providing, by the recommendation system hosted by the one or more servers, second content recommendation for display on the client device of the user;
• and
• in response to detecting a second event associated with the second content recommendation, generating, by the recommendation system hosted by the one or more servers, a second entry indicating a second event type of the second event, a second serve time of the model serving the second content recommendation, and a second train time of the model being trained using the historic user distribution data and the historic content distribution data, wherein the second event corresponds to an impression of the user viewing the second content recommendation.