Meta Platforms, Inc.

Bradley Ray Green

Jason Eric Brewer

Jinyi Yao

Li Ju

James Wah Hou Wong

Jun Li

Komal Kapoor

Jonathan Daniel Sorg

Jireh Yiwei Tan

Meiying Li

Apaorn Tanglertsampan

David Ian Ferguson

Amaç Herdagdelen

James Donovan

Yi Miao

Jun Sun

Hannah Marie Hemmaplardh

Deepak Chinavle

Feng Wang

Lars Seren Backstrom

Andrew Yaoshu Song

Leo Litterello Mancini

Fei Guo

Ashish Kumar Yadav

Xun Gong

Vishal Vusirikala

Kyle Philip Meyer

Pooja Jain

Jerry Ju

Gregory Joseph Klein

Brendon Elias Manwaring

Martin Schatz

Antoine Joseph Atallah

Neal Suresh Vora

Gaurav Dosi

Andrew Rocco Tresolini Fiore

Betty Yee Man Cheng

David Tomotsu Sasaki

Kai Wang

Ryan Farina

Chen Wang

Nigel Carter

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

In one embodiment, a method involves accessing training data, where the training data contains an ordered sequence of data associated with a plurality of entities, training one or more deep learning models to determine, from the ordered sequence of data, a first set of embeddings for each entity of the plurality of entities, where each entity has a plurality of entity attributes, determining, for each of the plurality of entity attributes, a corresponding initial embedding, training the one or more deep-learning models to refine the initial embeddings according to one or more criterion, generating one or more updated embeddings for each of the plurality of entities based on the refined initial embeddings of the plurality of entity attributes, and modifying the first set of embeddings based on the one or more updated embeddings.

Generating recommendations using a deep-learning model

Systems, methods, and non-transitory computer readable media are configured to apply a spectral clustering technique to at least a portion of a similarity graph to generate clusters of geographic sub-regions constituting geographic regions. A tf-idf technique is performed to determine pages of a social networking system associated with a geographic region as potential local suggestions for a user associated with a geographic sub-region in the geographic region. References to at least a portion of the pages are presented as local suggestions to the user.

Systems and methods to provide local suggestions based on spectral clustering

Systems, methods, and non-transitory computer readable media are configured to train a machine learning model. The training can be based on a training set of embeddings of a first type and a training set of embeddings of a second type. The machine learning model can be trained to receive an embedding of a second type and to output a corresponding embedding of the first type. A given embedding of the second type can be provided as input to the machine learning model. An embedding of the first type can be obtained from the machine learning model. The embedding of the first type can correspond to the given embedding of the second type.

Systems and methods for neural embedding translation

Systems, methods, and non-transitory computer readable media are configured to determine an interaction between a first entity and a first item. A second entity can be determined. The first entity can have formed a connection with the second entity on a social networking system. A belief that the second entity will interact with the first item can then be generated.

Systems and methods for belief learning

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.

Systems and methods for providing contextual recommendations for pages based on user intent

In one embodiment, an embedding is determined for each entity in a set of entities that is selected from a plurality of entities. Each embedding corresponds to a point in an embedding space, which includes points corresponding to embeddings of entities. The embeddings of the entities are determined using a deep-learning model. Embeddings are determined for each entity attribute in a set of entity attributes. Each of the entity attributes in the set is of an entity-attribute type and is associated with at least one entity. The entity-attribute embeddings are refined using the deep-learning model. The embeddings of the entities in the set of entities are modified based on the entity-attribute embeddings that are associated with the respective entity to obtain updated embeddings for each entity in the set. The updated embeddings include information regarding the entity attributes that are associated with the respective entities.

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 configured to generate session information based on information regarding items of a plurality of item types associated with interactions performed by active users of a social networking system. A graph is generated based on the session information. At least a first item of the items is assigned to a cluster based on similarity between the item and the cluster. The cluster is provided to a recommender system to facilitate selection of relevant information for potential presentation to a user.

Systems and methods for clustering items associated with interactions

Systems, methods, and non-transitory computer-readable media can determine at least one web site that is of interest to a user of the social networking system. One or more pages can be determined based at least in part on the web site, the one or more pages being accessible through the social networking system. At least one page recommendation that references at least one of the one or more pages can be provided to the user.

Systems, methods, and non-transitory computer-readable media can select a set of selected pages from a plurality of pages on a social networking system based on page selection criteria. A set of potential stories from the set of selected pages is aggregated. The set of potential stories are ranked based on ranking criteria. An administrator feed associated with a first page is generated, the administrator feed comprising a plurality of stories from the set of potential stories based on the ranking the set of potential stories.

Systems and methods for providing entity account administrator feeds

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.

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Snohomish, WA, United States of America

Bradley Ray Green