Abstract: A computer-implemented method for optimizing electronic content delivery for non-measurable users includes receiving a feature vector for each electronic content impression opportunity, receiving a feature vector for each delivered item of electronic content for measurable users, receiving an in-target indication for each delivered item of electronic content for measurable users, estimating a probability that an electronic content impression opportunity with a specified feature vector will meet targeting requirements based on the received feature vectors and the received in-target indications, receiving an in-target threshold value, generating an in-target rate control signal based on a number of total delivered items of electronic content for measurable users and a number of in-target delivered items of electronic content for measurable users, determining whether the estimated probability is greater than the in-target rate control signal, and generating conditions for delivering a new item of electronic cont

Abstract: The disclosed systems and methods provide a novel framework that provides mechanisms for predicting user actions of provided digital content based on an aggregation of user data. Conventional user tracking, and action prediction and recommendation systems have a lifespan that is ending in the short term due to new privacy laws. The disclosed framework enables personalized recommendations to be formulated for specific users based on an imputation from user data aggregated from a plurality of users. While anonymity is maintained, recommendations for predicted actions can be provided to the users and/or the providers of the content. The disclosed framework can scale the aggregated user data using a Naïve Bayes classifier, from which a logistic regression modeling can be performed to determine the predicted recommendation.

Abstract: Methods and apparatuses for delivering advertisements with electronic content provided over a network and, more specifically, to techniques for selecting among advertisements that are competing for a slot associated with electronic content that is to be delivered over a network, are presented herein. Selecting among advertisements that are competing for a slot is based, at least in part, on an estimated latency for each advertisement. The estimated latency of an advertisement is a prediction of what latency will be experienced if the advertisement is served. The estimated latency may be used as one of the parameters for determining which competing advertisement to place in a slot, where advertisements that are associated with low estimated latencies are favored. For example, if all other parameters are equal, a selection mechanism selects advertisement X over advertisement Y, if the estimated latency for advertisement X is less than the estimated latency of advertisement Y.