Abstract:
Systems, methods, and non-transitory computer-readable media can identify a target page and an advertising campaign comprising one or more advertisements associated with the target page. One or more users are identified for inclusion in a base audience based on page information associated with the target page. One or more users are identified for inclusion in an expanded audience based on expanded audience criteria. The advertising campaign is presented to a smart audience comprising the base audience and the expanded audience.
Abstract:
A online system generates and displays page promotion units to viewing users of the online system. The page promotion unit provides content to the viewing user to promote advertisement of a page that the viewing user administers or advertises for. The page promotion unit may be dynamically generated based on content of the page that the viewing user administers. The page promotion unit may also include suggested operational parameters for a campaign to display the content, which may include budget or suggested targeting criteria, as well as estimated reach for the campaign. The page promotion unit also provides an interface for the viewing user to promote the page and direct the viewing user to an interface for initiating an advertising campaign for the page, which may be prepopulated with content from the page promotion unit.
Abstract:
Systems, methods, and non-transitory computer-readable media can determine at least one content item to be promoted to one or more users. One or more tokens that describe the content item are determined. A set of interests are determined based at least in part on the one or more tokens using a trained machine learning model. At least one first interest from the set as a suggestion is provided for promoting the content item to users, wherein promoting the content item using the first interest causes the content item to be presented to users that are associated with the first interest.
Abstract:
The social networking system monitors implicit interactions between a user and objects of the social networking system with which the user has not established a connection. Based on the implicit interactions between the user and an object, the social networking system identifies a soft connection between the user and the object. The social networking system may then identify soft connections to include in a candidate list of soft connections to recommend to the user. The social networking system may also extract signals from the set of candidate list of soft connections, and may use the extracted signals to rank the soft connections in the list of candidate soft connections. The social networking system may then recommend soft connections to the user based on the rank associated with the soft connections in the candidate list of soft connections.
Abstract:
Systems, methods, and non-transitory computer readable media configured to determine seed content items based on interests of a user. Candidate content items can be determined for potential presentation to the user based at least in part on the seed content items. Features associated with the candidate content items can be processed to generate probabilities that the user will perform interactions with the candidate content items. Values can be assigned to the candidate content items based on the probabilities that the user will perform interactions with the candidate content items and the importance of the interactions. The values can be provided as bid values to an auction system to determine constraints regarding presentation of the candidate content items. Presentation of the candidate content items can be optimized.
Abstract:
The social networking system monitors implicit interactions between a user and objects of the social networking system with which the user has not established a connection. Based on the implicit interactions between the user and an object, the social networking system identifies a soft connection between the user and the object. The social networking system may then identify soft connections to include in a candidate list of soft connections to recommend to the user. The social networking system may also extract signals from the set of candidate list of soft connections, and may use the extracted signals to rank the soft connections in the list of candidate soft connections. The social networking system may then recommend soft connections to the user based on the rank associated with the soft connections in the candidate list of soft connections.