Facebook, Inc.

Meta Platforms, Inc.

Liang Xu

Goran Predovic

Chaochao Cai

Li Zhou

Aleksey Sergeyevich Fadeev

Yimin Song

Qing Li

Wei Wei

Stephane Taine

William Bullock

Logan Michael Gore

Weidong Wang

Artur Abdullin

Erjie Ang

Yongfeng Liu

Chang Liu

Zhonghua Qu

An online system receives information including a description of an event occurring in a mobile application and user identifying information associated with a user of the mobile application associated with the event via an SDK incorporated into the mobile application code of the mobile application. The online system determines whether the description corresponds to information maintained in the online system describing types of events capable of occurring in the mobile application. If the description corresponds to information describing a type of event capable of occurring in the mobile application, the online system logs the type of event in association with the user identifying information. Otherwise, the online system predicts a type of event corresponding to the event occurring in the mobile application based at least in part on the information received at the online system and logs the predicted type of event in association with the user identifying information.

Logging information describing a type of event occurring in a mobile application received via an SDK incorporated into mobile application code of the mobile application

An online system matches a user across multiple online systems based on image data for the user (e.g., profile photo) regardless whether the image data is from the online system, a different but related online system or a third party system. For example, to match the user across a social networking system and INSTAGRAM&#x2122; system, the online system compares the similarity between images of the user from both systems in addition to similarity of textual information in the user profiles on both systems. The similarity of image data and the similarity of textual information associated with the user are used by the online system as indicators of matched user accounts belonging to the same user across both systems. The online system applies models trained using deep learning techniques to match a user across multiple online systems based on the image data and textual information associated with the user.

Image based user identification across multiple online systems

Information describing deliveries of content items and user actions associated with the content items is stored. Each delivery is performed by an online publisher to a user. A user action associated with a content item performed by a target user is detected. Information describing a set of online publishers that delivered the content item to the target user is retrieved. For each online publisher of the set, a likelihood that the user action would have occurred without the online publisher's delivery of the content item to the target user is determined. An estimated increase in the likelihood that the user action occurred due to the online publisher's delivery of the content item to the target user is determined. A performance metric is determined for the online publisher, wherein ratios of performance metrics for the set of online publishers are related based on corresponding ratios of the estimated increases in likelihoods.

Determining performance metrics for delivery of electronic media content items by online publishers

An online system promotes physical store visits by presenting users with content items for a physical store location and subsequently logs visits of online system users to the physical store location to track performance of a campaign associated with the presented content item. The online system registers attention events associated with the presented content items presented to users on third party publishing sites via tracking pixels and registers attention events as store front visit conversion events if, within a predetermined period of time from a valid attention event, a user has subsequently gone in and visited the physical store front location.

Physical store visit attribution

An online system maintains an identity graph having links between different types of user identifying information (e.g., email addresses, phone numbers, user identifiers) describing various users of the online system. Based on information received from various sources describing relationships between different types of user identifying information describing a user, the online system generates confidence values for each link between different types of user identifying information. In some embodiments, a confidence value accounts for an amount of time since information describing a relationship between different types of user identifying information was received from a source. If the confidence value of a link between different types of user identifying information equals or exceeds a threshold value, the online system determines the different types of user identifying information are correlated with each other, allowing the online system to correlate user identifying information without storing user identifying information received from sources.

Determining correlations between types of user identifying information maintained by an online system

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.

User targeting using an unresolved graph

An online system determines one or more metrics describing consumption of content by various users by identifying users of the online system capable of being identified based on information received from multiple client devices. For example, the online system identifies users associated with user identifiers that are also associated with other types of identifying information (e.g., cookies, device identifiers). From the identified users, the online system generates a set of users based on a distribution of characteristics. The distribution of characteristics may be determined by the online system as characteristics of a group of users or received by the online system from a third party system and describes characteristics of users of the third party system. Based on interactions with content by users in the set, the online system determines one or more metrics describing consumption of content.

Accounting for bias of user characteristics when determining consumption of content by online system users

An online system maintains characteristics for its users and may access characteristics of users maintained by a third party system. The online system may select content for a user of the third party system based on characteristics maintained by the third party system. If the third party system does not maintain a characteristic for its users, the generates a model predicting the characteristic for third party system users based on a set of online system users identified based on characteristics of third party system users. The online system clusters third party system users based on the predicted characteristic for other third party system users connected to the third party system user. Using verified characteristics for third party system users from a trusted third party system, the online system determines an accuracy of the predicted characteristic for third party system users in a cluster.

Predicting characteristics of users of a third party system that communicates with an online system and determining accuracy of the predicted characteristics

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.

Predicting household demographics based on image data

An online system predicts gender, age, interests, or other demographic information of a user based on image data of the user, e.g., profile photos, photos the user posts of him/herself within an online system, and photos of the user posted by other users socially connected with the user, and textual data in the user's profile that suggests age or gender (e.g., like or dislikes similar to a population of users of an online system). The online system similarly predicts a user's interests based on the photos of the user. The online system applies one or more models trained using deep learning techniques to generate the predictions. The online system uses the predictions to build more information about the user in the online system, and provide improved and targeted content delivery to the user that may have disparate information scattered throughout different online systems.

Image based prediction of user demographics

An online system associates a user with a characteristic attribute of a geographic area in response to the user visiting the geographic area. The geographic area is identified based on visits by users of the online system, and attributes of entities associated with locations within the geographic area. A characteristic attribute is identified from the obtained attributes. A visit to the geographic area by a first user not associated with the characteristic attribute is identified. In response to identifying that the first user has visited the geographic area, the first user is associated with the characteristic profile attribute. Based at least in part on the association between the characteristic profile attribute and the first user, a content item is sent to a client device for presentation to the first user.

Location-based inference of user characteristics

Online system users interact with one or more third party systems, with the online system maintaining an account for each of its users and each third party system maintaining a third party account for each of its users. The online system compares information in a user's account to accessible information in third party accounts and establishes connections between the user's account and third party accounts based on the comparisons, a connection including a confidence level indicating a likelihood of a third party account being associated with the user of the online system corresponding to the user's account. Similarly, the online system compares information in different third party accounts and establishes connections between different third party accounts based on the comparisons including includes a confidence level indicating a likelihood of a third party account and an additional third party account being associated with the same user.

Establishing connections between third party accounts maintained by various third party systems and between third party accounts and accounts maintained by an online system

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Bellevue, WA, United States of America

Liang Xu