Branch Metrics, Inc.

Facebook, Inc.

Sofus Attila Macskassy

Eric J Glover

Dmitri Ariel Gaskin

Kan Yu

Wenchang Yan

Alexander Austin

Cheng-chao Yang

William Lindemann

Jonathan D Chang

Deepayan Chakrabarti

Vincent Gonguet

Stanislav Funiak

Aude Hofleitner

Ahmad Abdulmageed Mohammed Abdulkader

Aruna Bharathi

Benjamin Ray

Steven James Jarrett

Zhiliang Ma

A method includes receiving first event data from a device, the first event data including a first identifier (ID) that identifies the device, web browser metadata, and data associated with an application state. The method includes generating a first persona including the first ID and a first event identifier that identifies a first user action. The method includes receiving second event data, the second event data including device metadata and a second ID that identifies the device. The method includes generating a second persona based on the second event data, the second persona including the second ID and a second event identifier that identifies a second user action. Additionally, the method includes determining that the first and second personas are associated with the device based on matches between the browser metadata and device metadata and transmitting a response to the device configured to route the device to the application state.

Matching and attribution of user device events

A method includes receiving a request from a user device accessing a webpage, the request including a webpage uniform resource locator (URL) and a user device identifier. The method includes retrieving a list of events associated with the user device based on the device identifier. The method further includes retrieving sets of rules. Each set of rules indicates events and URLs that satisfy the set of rules. Each set of rules is associated with a template that includes link rendering data for rendering a link on the user device. The method includes identifying a set of rules that is satisfied by the received URL and events, transmitting link rendering data associated with the identified set of rules to the user device, and transmitting link routing data to the user device. The link routing data is configured to route the user device to an application state corresponding to the webpage.

Generating custom application links

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

Systems, methods, and non-transitory computer-readable media can acquire an image that depicts at least one character. A set of pixels, within the image, through which the at least one character is depicted can be identified. At least one linear portion, within the image, can be identified based on the set of pixels. For each sub-portion within the at least one linear portion, a respective first confidence score representing a respective first likelihood that a respective sub-portion depicts the at least one character can be determined.

Systems and methods for incremental character recognition to recognize characters in images

At least one embodiment of this disclosure includes a method of inferring attribute labels for a user in a social networking system based on the user's social connections and user-specified attribute labels in the social networking system. The method can include: establishing variational equations based on attribute labels of nodes in an ego network in a social graph of a social networking system; determining likelihood scores for at least a portion of the attribute labels of neighboring nodes from a focal user node in the ego network based on user-specified attribute labels from the social networking system; and calculating probability distributions of possible attribute labels for the focal user node of the ego network based on the variational equations and the likelihood scores.

Label inference in a social network

Growing community of inventors

Palo Alto, CA, United States of America

Sofus Attila Macskassy