Meta Platforms, Inc.

Facebook, Inc.

Logan Michael Gore

Goran Predovic

Chaochao Cai

Liang Xu

Xiao Zhang

Qing Li

An online system receives information describing presentations of a content item to individuals. The online system determines impression frequencies for online system users included among the individuals, de-biases this information, and trains a set of machine-learning models to infer a reach for each of multiple impression frequencies using the de-biased information. The online system predicts the reach for each impression frequency using the models, determines an inferred total number of presentations of the content item based on the inferences, and compares the inferred total number of presentations to a known total number of presentations of the content item. If the inferred total number of presentations is greater than the known total number of presentations, the online system adjusts the inferred reach for a set of impression frequencies by reducing the inferred reach for a highest impression frequency and by increasing the inferred reach for one or more lower impression frequencies.

Inferring a reach of a content item presented to individuals by one or more content publishers for a set of impression frequencies

A method for predicting demographic information for a common user that is associated with a plurality of unresolved identifiers. An unresolved identifier defines a context in which a client device accesses one or more online systems, the context not determined to be associated with a specific user. The method comprises identifying a set of unresolved identifiers, and identifying information describing one or more access events associated with each unresolved identifier. For each pair of unresolved identifiers, a similarity score for the pair is determined based on the identified information. Responsive to the similarity score exceeding a threshold similarity score, the pair of unresolved identifiers is clustered, the clustering indicating a prediction that the pair of unresolved identifiers are associated with a common user. Predicted demographic information is determined for each unresolved identifier in the cluster. Based on predicted demographic information determined for each unresolved identifier in the cluster, aggregate demographic information is determined for the common user.

Predicting demographic information using an unresolved graph

An online system, such as an online content distribution system, receives information describing presentations of a content item. The online system identifies online system users included among individuals presented with the content item, retrieves information describing a household of each user, and de-biases this information to adjust for differences between the population of online system users and the individuals to whom the content item was presented. The online system then trains a set of machine-learning models to infer a reach of the content item for each of multiple households using the de-biased information. Using the machine-learning models, the online system infers a reach of the content item for each of the households.

Inferring a reach of a content item presented to individuals by one or more content publishers for a set of households

A method for providing content items to one or more client devices associated with at least one unresolved identifier. An unresolved identifier defines a context in which a client device accesses one or more online systems, the context not determined to be associated with a specific user. The method comprises identifying a set of unresolved identifiers, and identifying information describing one or more access events associated with each unresolved identifier. For each pair of unresolved identifiers, a similarity score for the pair is determined based on the identified information. Responsive to the similarity score exceeding a threshold similarity score, the pair of unresolved identifiers is clustered, the clustering indicating a prediction that the pair of unresolved identifiers are associated with a common user. Based on this clustering, a content item is displayed on or more user devices associated with at least one unresolved identifier of the set of unresolved identifiers.

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Seattle, WA, United States of America

Logan Michael Gore