Facebook, Inc.

Meta Platforms, Inc.

Cassidy Jake Beeve-Morris

Li Ju

Zhen Li

Eric Hsin-Chun Huang

Yuan Liang

Yun Liu

Chang Su

Shuo Li

Jonathan Mooser

Chin Lung Fong

Matthew Feldman

Halil Bayrak

Yining Wu

Elizabeth Patton Boles

Aishwarya Rajagopal

Leqiang Li

Zachary Zhang

A social networking system allows users to create and join groups in the social networking system, in which each group is associated with one or more interests shared by members of the group. To select content for presentation to a viewing user of the social networking system who is a member of a group, the social networking system may determine one or more topics associated with the group based at least in part on a classification system and on information associated with objects maintained in the social networking system that are similar to the group. The topic(s) associated with the group also may be determined based on information associated with the group, with members of the group, and/or with objects associated with the group. The social networking system may then select one or more content items associated with the topic(s) for presentation to the viewing user via a group page.

Selecting content for presentation to a user of a social networking system based on a topic associated with a group of which the user is a member

An online system identifies a candidate content item eligible for presentation to a viewing user of the online system, in which the candidate content item includes media (e.g., an image, a video, etc.). The online system identifies one or more media attributes for the media, such as color saturation, tone, brightness, sharpness, contrast, etc. The online system also predicts a value of a performance metric for the candidate content item that indicates a likelihood of user engagement with the candidate content item by the viewing user. For each modification that may be made to a media attribute, the online system predicts a change to the value of the performance metric. Based on the predicted change, the online system determines an optimal set of media attributes associated with a maximum predicted value of the performance metric. The online system modifies the media based on the optimal set of media attributes.

Optimizing user engagement with content based on an optimal set of attributes for media included in the content

An online system applies content policies regulating presentation of sponsored content to its users. For example, content policies may prevent the presentation of sponsored content items in certain positions content feeds. The online system may relax a content policy when generating a content feed for a user based on characteristics of a user. For example, the online system generates a model determining a tolerance of the user for sponsored content, and relaxes one or more content policies if the tolerance of the user for sponsored content equals or exceeds a threshold. As another example, the online system determines whether to relax one or more content policies based on a comparison of a historical amount of compensation received from the user and an expected amount of compensation from presenting content items violating a content policy.

Relaxing policy rules for regulating the presentation of sponsored content to a user of an online system based on characteristics of the user

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Cassidy Jake Beeve-Morris