Facebook, Inc.

Meta Platforms, Inc.

Komal Kapoor

Bradley Ray Green

Feng Wang

James Donovan

Hannah Marie Hemmaplardh

Apaorn Tanglertsampan

Meiying Li

Vishal Vusirikala

Jason Eric Brewer

Jonathan Daniel Sorg

Gaurav Singh Thakur

David Tomotsu Sasaki

Kai Wang

Ryan Farina

Haotian Wang

Systems, methods, and non-transitory computer-readable media can be configured to determine a page embedding for each page in a sequence of pages visited by a user. A pooled page embedding can be determined based on the page embeddings for the sequence of pages visited by the user. One or more page recommendations for the user can be determined based at least in part on the pooled page embedding.

Systems and methods for recommending pages

An online system maintains pages and accesses a graph of nodes representing the pages. Each node is labeled to indicate that a corresponding page is for a real-world entity, an imposter of the real-world entity, or a derived entity complying with or violating a policy. The online system retrieves machine-learning models, each of which is trained based on labels for a set of the nodes and features of corresponding pages. A first model predicts whether a page is for a derived entity based on features of the page. Responsive to predicting the page is not for a derived entity, a second model predicts whether the page is for a real-world entity or an imposter based on features of the page. Responsive to predicting the page is for a derived entity, a third model predicts whether the derived entity complies with or violates the policy based on features of the page.

Detecting a page for a real-world entity, an imposter of a real-world entity, or a non-real-world entity that complies with or violates a policy of an online system

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.

Systems and methods for providing service directory predictive search recommendations

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.

Systems and methods for providing a service directory for automatic search engine indexing

Systems, methods, and non-transitory computer-readable media can determine a plurality of candidate entities for recommendation to a user of a social networking system. A predicted activity objective value model configured to calculate activity stores for candidate entities is established. The activity score is indicative of the probability of future activity on the social networking system by a candidate entity. A first activity score is determined for each of the plurality of candidate entities based on the predicted activity object value model and a first set of feature values. A second activity score is determined for each of the plurality of candidate entities based on the predicted activity object value model and a second set of feature values that is different from the first set of feature values. A first entity is selected of the plurality of candidate entities based on the first and second activity scores.

Systems and methods for predicting page activity to optimize page recommendations

Systems, methods, and non-transitory computer-readable media can determine a change made to a page that is accessible through a social networking system. A page update story that describes the change can be generated. The page update story to be published through the social networking system.

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Bellevue, WA, United States of America

Komal Kapoor