Facebook, Inc.

Meta Platforms, Inc.

Li Ju

Bradley Ray Green

Yi Miao

Pawel Bara

Ramanathan Palaniappan

Zhen Li

Jireh Yiwei Tan

Yuan Liang

Matthew Bryan Folz

Jonathan Ma

David Ian Ferguson

Lars Seren Backstrom

Jun Li

Eric Hsin-Chun Huang

Jun Sun

Eric Carl Ertmann

Cassidy Jake Beeve-Morris

Adit Vaidya

Felix Wong

Yun Liu

Chang Su

Xun Gong

Kyle Philip Meyer

Pooja Jain

Kelly Jayne Mayes

Yanping Li

Congle Zhang

Jingxian Li

Chen Wang

Alkarim Nasser

Ritika Prasad

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

Systems, methods, and non-transitory computer readable media configured to receive an initial targeting for an advertisement. The advertisement can promote a group of a social networking system. A refined targeting for the advertisement can be generated. The advertisement based on the refined targeting can be delivered.

Systems and methods for promoting groups

An online system provides group recommendations by applying a set of sourcing rules to identify a plurality of candidate groups and then generating scores for the candidate groups. The sourcing rules can be configured to identify a relatively small subset of the groups maintained by the online system. After the candidate groups are identified, the online system generates a score for each candidate group, ranks the candidate groups based on the scores, and sends high-ranking candidate groups to the target user to be displayed as recommended groups. As a result, the online system generates a smaller number of scores, which advantageously allows for the online system to provide group recommendations to users in a more computationally efficient manner.

Recommendations for online system groups

An online system predicts the user's activity on the online system during a future time interval (e.g., the next day). The system collects activity data, such as actions that the user has taken on the system. The system predicts whether the user is likely to be active during the future time interval based on features extracted from the activity data. This system determines selection of notifications and delivery of notifications based on the predicted time when the user is likely to be active on the online system. The system further records the user's past interactions with notifications, such as whether the user viewed the notification, whether the user interacted with a content item associated with the notification, and so on. If system determines a rate of delivery of notifications to the user based on the frequency of past user interactions with notifications.

Adjusting pacing of notifications based on interactions with previous notifications

A social networking system maintains various groups that each include one or more users and maintains information describing interactions by users with the social networking system. The social networking system selects various content items provided to groups including the user based on amounts of interaction with content items provided to groups including the user by other social networking system users as well as amounts of time since the social networking system obtained various content items provided to the groups. Information describing the selected content items is presented to the user by the social networking system outside of pages of content maintained by the social networking system for groups to which the selected content items were provided.

Selecting content items provided to groups including a social networking system user based on interaction with the content items by social networking system users

A social networking system identifies a set of groups of which a user of the social networking system is not a member and selects a group for recommendation to the user based on a predicted affinity of the user for each identified group. The social networking system computes an affinity score of the user for each of one or more members of the selected group based at least in part on information describing a set of connections between the user and each member. Based on the scores, the social networking system selects a set of members of the group and generates a recommendation unit, wherein images associated with the selected members are arranged based on their scores and overlay at least a portion of an image associated with the group. The recommendation unit is communicated to the user to suggest that the user join the recommended group.

Recommending a group to a user of a social networking system based on affinities of the user for members of the group

A social networking system selects a set of groups for presentation to a user of the social networking system. To select groups, the social networking system determining scores for various groups representing a likelihood of the user interacting with the groups. When determining a score for a group, the social networking system accounts for a likelihood of the user providing content to the group as well as the user accessing or viewing content associated with the group. Based on the scores, one or more groups are selected and presented to the user. Additionally, the social networking system may apply one or more diversity rules so that the selected groups have a variety of characteristics.

Identifying groups for a social networking system user based on group characteristics and likelihood of user interaction

Systems, methods, and apparatuses for tracking trends at an online presence are described. In some embodiments, an interactivity data structure and a tenured interactivity data structure are utilized in determining trends quickly.

Real-time trend detection in a social network

A social networking system maintains various groups that each include one or more users and maintains information describing interactions by users with the social networking system. Based on interactions with the social networking system by the user, the social networking system determines a time interval when the user most frequently interacts with the social networking system. Additionally, the social networking system selects various content items provided to groups including the user based on amounts of interaction with content items provided to groups including the user by other social networking system users. During the time interval when the user most frequently interacts with the social networking system, information identifying a set of the selected content items is presented to the user via the social networking system.

Accounting for user interaction with a social networking system when notifying the user of content provided to a group maintained by the social networking system including that user

Delivering notifications based on prediction of user activity

A social networking system selects a set of groups for presentation to a user of the social networking system. To select groups, the social networking system determines scores for various groups representing a likelihood of the user interacting with the groups. The social networking system may identify a set of groups based on interactions between the user and various groups occurring during a specific time interval and determine scores for groups in the set. When determining a score for a group, the social networking system accounts for times associated with interactions between the user and various groups. Based on the scores, one or more groups are selected and presented to the user. Additionally, the social networking system may identify a time to present the selected one or more groups to the user based on prior user interactions with various groups.

Identifying groups for a social networking system user based on interactions by the user with various groups

A social networking system selects a set of groups for presentation to a user of the social networking system. To select groups, the social networking system identifies candidate groups and selects the set of groups from the candidate groups. To identify certain candidate groups, the social networking system determines a location associated with various groups based on locations associated with users included in the group. For example, the social networking system determines a centroid of a group based on locations associated with users included in the group and associates the centroid with the group if at least a threshold percentage of distances between locations associated with users included in the group and the centroid do not exceed a threshold distance. Groups associated with locations within a threshold distance of a location associated with the user are identified as candidate groups.

Identifying groups for recommendation to a social networking system user based on user location and locations associated with groups

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Issaquah, WA, United States of America

Li Ju