Facebook, Inc.

Aruna Bharathi

Andrew Garrod Bosworth

Vincent Gonguet

Sofus Attila Macskassy

Aude Hofleitner

Steven James Jarrett

Zhiliang Ma

Systems, methods, and non-transitory computer-readable media can collect past user information and churn data for a plurality of users. A churn prediction model is trained using the past user information and churn data. A churn propensity score is calculated for a present user based on the churn prediction model, the churn propensity score indicative of the likelihood of the present user to churn.

Systems and methods for churn prediction

An influence metric describing the influence of a social networking system object on social networking system users is determined based on affinities between the users and the object. For example, affinities between the associated users and the object are combined to determine the influence metric. Content may be selected for presentation to users based in part on influence metrics of the content. Additionally, influence metrics of objects associated with a user may be combined to determine the relevance of objects associated with the user, which may also be used to select content for presentation to the user.

Determining influence in a social networking system

Determining object relevance in a social networking system

Growing community of inventors

San Jose, CA, United States of America

Aruna Bharathi