Facebook, Inc.

Meta Platforms, Inc.

William Bullock

Li Zhou

Anh Phuong Bui

Liang Xu

Frederick Ross Leach

Sanjay Kanaka Sai Tirupattur Saravanan

Bradley Hopkins Smallwood

In one embodiment, a method includes generating predicted locations of each of a plurality of network addresses, wherein each predicted location is associated with a time stamp representing an age of the predicted location, determining a weighting factor representing a probability that at least one of the predicted locations of the network address corresponds to a true location of the network address based on location-related features associated with each network address and the time stamps, determining a weight for each predicted location based on at least the weighting factor, wherein the weight represents a probability that the predicted location corresponds to the true location of the network address, and providing one or more of the predicted locations that correspond to a particular network address based on the respective weights of the predicted locations in response to a request to identify a geographic location for the particular network address.

Determining geographic locations of network devices

An online system receives a request from an online system user to present a content item associated with an action that may be performed on a third party website not associated with the user. The online system identifies a set of third party websites on which the action may be performed based on information provided by content publishers associated with the websites describing performances of the action on the websites. The online system predicts a likelihood a viewing user of the online system presented with the content item will perform the action on each third party website based on the information provided by the content publishers and selects a website associated with a highest predicted likelihood the viewing user will perform the action on the website. The online system generates the content item including a link to the selected website and provides the content item for presentation.

Selecting a third party website on which an action associated with a content item may be performed

The present disclosure is directed toward systems, methods, and non-transitory computer readable media for generating an identity resolution model from a ground truth data set to accurately match users across one or more digital content providers to perform analyses of user activities across the one or more digital content providers. For example, the systems described herein can generate a ground truth data set of known users and utilize the ground truth data set to generate an identity resolution model for one or more digital content providers based on predicted user identities. Furthermore, in one or more embodiments, the systems utilize the identity resolution model to accurately resolve and match user identities between one or more digital content providers and assign universal identifiers to the user identities. Moreover, the disclosed systems can utilize the universal identifiers to provide analytical insights of user actions between the one or more digital content providers.

User identification system

In one embodiment, a method includes receiving one or more communication network addresses and one or more geographic locations of each network address, determining one or more location-related features based on each network address, generating one or more predicted locations of the network address, each predicted location corresponding to one of the first geographic locations of the network address, and each predicted location being associated with a time stamp representing an age of the predicted location, determining, based on the location-related features and the time stamps, a weighting factor representing a probability that at least one of the predicted locations of the network address corresponds to a true location of the network address, and determining, for each of the predicted locations, a weight based on at least the weighting factor, wherein the weight represents a probability that the predicted location corresponds to the true location of the network address.

Embodiments include one or more client devices accessible by users, an online system, and one or more partner systems such that the online system is able to identify a user of the online system across different devices and browsers based on the user activity that occurs external to the online system. A user performs user actions (e.g. purchase a product) on a web page of a partner system and may provide personally identifiable information (PII) to the partner system. The partner system provides the hashed PII and user actions performed by the user to the online system. The online system identifies a user profile on the online system by matching personal information in the user profile to the hashed PII. The online system generates a confidence score indicating a likelihood that the identified user of the online system is the individual that performed the external user action.

People-based user synchronization within an online system

An online system predicts household features of a user, e.g., household size and demographic composition, based on image data of the user, e.g., profile photos, photos posted by the user and photos posted by other users socially connected with the user, and textual data in the user's profile that suggests relationships among individuals shown in the image data of the user. The online system applies one or more models trained using deep learning techniques to generate the predictions. For example, a trained image analysis model identifies each individual depicted in the photos of the user; a trained text analysis model derive household member relationship information from the user's profile data and tags associated with the photos. The online system uses the predictions to build more information about the user and his/her household in the online system, and provide improved and targeted content delivery to the user and the user's household.

Growing community of inventors

Palo Alto, CA, United States of America

William Bullock