Abstract: Systems, methods, and non-transitory computer readable media can determine one or more actions that a user is likely to take on a page associated with a social networking system, based on one or more first machine learning models. One or more card types that correspond to the one or more actions can be ranked based on a second machine learning model. One or more cards can be generated based on the ranked card types, and each card can include a recommended action associated with the page.

Abstract: An online system maintains a web page associated with one or more page administrators. The online system trains a machine learning model to determine a likelihood of a page administrator account accepting a request for the online system to present content about the web page to other users of the online system. The model uses features extracted from data about the page administrator accounts on the online system, the page administrator interactions with the online system, and the web page. The online system selects one or more page administrator accounts and sends them requests based on the determined likelihood scores. The online system delivers content associated with the web page to users of the online system based on a response to the request.

Abstract: Systems, methods, and non-transitory computer-readable media can train a machine learning model to determine predictive search recommendation based on search prediction information. Search prediction information associated with a user is provided to the machine learning model. A predictive search recommendation is presented to the user based on the machine learning model and the search prediction information. A search is performed based on the predictive search recommendation for one or more search results associated with entity pages on a social networking system.

Abstract: Systems, methods, and non-transitory computer-readable media can present a service directory landing page comprising a plurality of selectable service category options associated with a plurality of pre-defined service categories. A search results page is presented, including one or more search results based on search criteria. Each of the one or more search results is associated with an entity page of a social networking system. The service directory landing page and the search results page are accessible without logging into the social networking system. Each entity page on the social networking system is accessible only when logged into the social networking system.

Abstract: An online system maintains a web page associated with one or more page administrators. The online system trains a machine learning model to determine a likelihood of a page administrator account accepting a request for the online system to present content about the web page to other users of the online system. The model uses features extracted from data about the page administrator accounts on the online system, the page administrator interactions with the online system, and the web page. The online system selects one or more page administrator accounts and sends them requests based on the determined likelihood scores. The online system delivers content associated with the web page to users of the online system based on a response to the request.

Abstract: Systems, methods, and non-transitory computer readable media can determine one or more actions that a user is likely to take on a page associated with a social networking system, based on one or more first machine learning models. One or more card types that correspond to the one or more actions can be ranked based on a second machine learning model. One or more cards can be generated based on the ranked card types, and each card can include a recommended action associated with the page.

Abstract: An online system maintains a web page associated with one or more page administrators. The online system trains a machine learning model to determine a likelihood of a page administrator account accepting a request for the online system to present content about the web page to other users of the online system. The model uses features extracted from data about the page administrator accounts on the online system, the page administrator interactions with the online system, and the web page. The online system selects one or more page administrator accounts and sends them requests based on the determined likelihood scores. The online system delivers content associated with the web page to users of the online system based on a response to the request.

Abstract: Systems, methods, and non-transitory computer-readable media can present a service directory landing page comprising a plurality of selectable service category options associated with a plurality of pre-defined service categories. A search results page is presented, including one or more search results based on search criteria. Each of the one or more search results is associated with an entity page of a social networking system. The service directory landing page and the search results page are accessible without logging into the social networking system. Each entity page on the social networking system is accessible only when logged into the social networking system.

Abstract: Systems, methods, and non-transitory computer-readable media can train a machine learning model to determine predictive search recommendation based on search prediction information. Search prediction information associated with a user is provided to the machine learning model. A predictive search recommendation is presented to the user based on the machine learning model and the search prediction information. A search is performed based on the predictive search recommendation for one or more search results associated with entity pages on a social networking system.

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: Systems, methods, and non-transitory computer-readable media can determine a content item to be promoted to one or more users of the social networking system. At least one seed content item in the social networking system that is similar to the content item to be promoted can be determined. A set of interests can be determined based at least in part on the at least one seed content item. One or more interests in the set can be determined as suggestions for promoting the content item to users, wherein promoting the content item using a first interest causes the content item to be presented to users of the social networking system that are associated with the first interest.