Facebook, Inc.

Meta Platforms, Inc.

Jinyi Yao

Bradley Ray Green

James Wah Hou Wong

Wenzhe Jiang

Hannah Marie Hemmaplardh

Ashish Kumar Yadav

Ahmed Magdy Hamed Mohamed

Jason Eric Brewer

Meiying Li

Yixin Li

Wentao Luan

Betty Yee Man Cheng

Jing He

Systems, methods, and non-transitory computer-readable media can determine a user embedding for a user of a content provider based on one or more machine learning models. A job embedding for a job posting published through the content provider can be determined based on one or more machine learning models. A determination can be made of a threshold level of similarity between the user embedding and the job embedding. The job posting can be provided as a recommendation to the user through the content provider.

Systems and methods for recommending job postings

Systems, methods, and non-transitory computer-readable media can determine at least one real-world scene being captured by one or more cameras of the computing device while being operated by a user, wherein the at least one real-world scene corresponds to a geographic location associated with an entity. At least one real-world surface can be determined based at least in part on the captured real-world scene. Content related to the entity can be obtained from a content provider. The content related to the entity can be presented on the at least one real-world surface through an augmented reality interface.

Systems and methods for providing content

Systems, methods, and non-transitory computer-readable media can determine at least one scenario that applies to a user of a social networking system based at least in part on features associated with the user. One or more groups of content recommendations associated with the at least one scenario can be determined. Each group of content recommendations can include a set of content items that relate to the at least one scenario. The one or more groups of content recommendations can be provided to the user as recommendations.

Systems and methods for recommending content

A social networking system recommends objects, such as pages, of the social networking system to users of the social networking system based on the location of the user. The social networking system obtains location information identifying the location of the user. Based on the location of the user, the social networking system identifies levels of geographical partitions encompassing the location of the user. For each level of geographical partitions, the social networking system accesses relevant objects of the social networking system with connections to users located within the level of geographical partitions. The social networking system may have determined a term frequency-inverse document frequency (tf-idf) value for each relevant object. Based on the number of connections and the tf-idf value associated with each relevant object, the social networking system merges the relevant objects accessed at each level into a set of relevant objects to recommend to the user.

Recommending objects to a user of a social networking system based on the location of the user

Systems, methods, and non-transitory computer readable media are configured to generate multiple channel embeddings for a page of a social networking system. The multiple channel embeddings can be mapped to a shared embedding space. A page embedding for the page of the social networking system can then be generated.

Systems and methods for page embedding generation

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.

Delivering content promoting a web page to users of an online system

Systems, methods, and non-transitory computer-readable media can determine a profile model for a page that is accessible through the social networking system, the profile model describing one or more modal characteristics of users of the social networking system that have fanned the page. A determination can be made that the page should be recommended to a first user of the social networking system based at least in part on the profile model. At least one page recommendation that references the page can be provided to the first user.

Systems and methods for filtering page recommendations

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.

Recommending objects to a user of a social networking system based on implicit interactions between the user and the recommended objects

Systems, methods, and non-transitory computer-readable media can receive page information associated with a page and user information associated with a user associated with the page. Confidence scores are calculated for a plurality of categories based on the page information and the user information, wherein a confidence score for a category is indicative of a likelihood that the category is relevant to the page. One or more categories of the plurality of categories are selected based on the confidence scores. The one or more categories are presented to the user as category recommendations.

Systems and methods for automated page category recommendation

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.

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Issaquah, WA, United States of America

Jinyi Yao