Pinterest, Inc.

John William Gupta Egan

Bo Zhao

Samuel Seth Weisfeld-Filson

Burkay Birant Orten

Koichiro Narita

Systems and methods for generating user notifications to a set of users of a social networking service is presented. For each user of a set of users of the social networking service, one or more machine learning models selects an optimal notification channel, an optimal notification template, and optimal personalization content for configurable elements of a selected notification template. Each of these determinations/selections is made according to and based on a likelihood of increased user engagement with the social networking service. Upon determining the notification channel, notification template, and personalizations to the template, the notification is generated and sent to the corresponding user.

Optimal notification

Described are systems and methods for generating recommendation campaigns that optimize for both a desired short-term user behavior and a desired long-term user behavior. In comparison to existing techniques that focus on targeting advertisements or recommendations to specific individuals with a single goal of receiving an interaction with the advertisement from that individual (i.e., a desired short-term behavior), the disclosed implementations consider the long-term user behavior, such as increased visits to a website during a long-term rage, and generate a recommendation campaign that also optimizes for that desired long-term user behavior.

Recommendation campaigns based on predicted short-term user behavior and predicted long-term user behavior

Systems and methods for identifying a selected set of users of a social networking service which, upon issuing a notice regarding a re-post activity of an item of content, will likely result in heightened user interaction with the service. Upon receiving a notice of a re-post action by a first user, the social networking service identifies a first set of users of the service that have posted that item of content. Scores are associated with the users of the first set of users, the scores indicating a likelihood of a user that, if notified of the re-post action of the first user, will result in heightened user interaction with the social networking service. A subset of users of the first set of users are identified according to their associated scores and a notice of the re-post action by the first user is sent to the users of this subset of users.

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Mountain View, CA, United States of America

John William Gupta Egan