Facebook, Inc.

Microsoft Technology Licensing, LLC

Daniel Bernhardt

Ian Douglas Hegerty

Antonino Gulli

Feng Liang

Michael Kaisser

Agnieszka Anna Podsiadlo

Rui Andre Augusto Ferreira

Hongcheng Zhu

Victor Lassance Oliveira E Silva

Daniel Voinea

Tomasz A Marciniak

Marcus Holland-Moritz

Xavier Sloane Dupre

Rafal Krzysztof Sadziak

Systems, methods, and non-transitory computer-readable media can receive a social network post associated with a poster. The social network post is analyzed, and one or more potential viewers are ranked based on viewer ranking criteria. A predicted relevant audience is determined based on the ranking of the one or more potential viewers.

Systems and methods for social network post audience prediction and selection

This disclosure generally covers systems and methods that schedule posts of one or more images, videos, or other digital content items through an account of a social networking system in a way that optimizes user engagement. In particular, certain embodiments of the disclosed systems and methods determine when to post digital content items by calculating data-driven user engagement scores and updating those scores with data tracked by the social networking system.

Optimizing audience engagement with digital content shared on a social networking system

In one embodiment, a method includes, receiving, from a client system of a user, a search query including n-grams. The method includes associating each n-gram with verticals based on an analysis of the n-grams by language models. The method includes determining, for each n-gram, if a bloom filter for a vertical associated with the n-gram indicates, based on sub-bloom filters of the bloom filter, the n-gram does exist or does not exist in a set of object names associated with the vertical. Each sub-bloom filter is associated with a subset of the set of object names and indicates the n-gram does exist or does not exist in its subset of object names. The method includes sending, to the client system, an indication that an n-gram of the n-grams is misspelled if a bloom filter indicates the n-gram does not exist in the set of object names associated with the vertical.

Social-based spelling correction for online social networks

In one embodiment, a method includes receiving a search query including one or more n-grams, determining for each n-gram if a contextual speller model indicates the n-gram is misspelled, identifying for each misspelled n-gram one or more variant-tokens based at least on the search query and a contextual speller model, generating one or more unique combinations of the n-grams and variant-tokens, where each unique combination includes a variant-token corresponding to each misspelled n-gram, calculating a relevance-score for each unique combination based at least in part on the search query and the contextual speller model, generating one or more corrected queries, where each corrected query includes a unique combination having a relevance-score greater than a threshold relevance-score, and sending one or more of the corrected queries to a user for display.

Contextual speller models on online social networks

In one embodiment, a method includes receiving a search query including one or more n-grams, where the n-grams include one or more misspelled n-grams, identifying one or more variant-tokens for each misspelled n-gram, calculating a feature value for each identified variant-token based at least on the identified variant-token, the misspelled n-gram, and one or more variant-tokens corresponding to one or more n-grams preceding the misspelled n-gram, generating one or more unique combinations of the n-grams and variant-tokens, calculating a sequence-score for each unique combination based at least in part on the calculated feature values of the variant-tokens of the unique combination, generating one or more corrected queries, where each corrected query includes a unique combination having a sequence-score greater than a threshold sequence-score, and sending one or more of the corrected queries to a user for display.

Spell correction with hidden markov models on online social networks

One or more techniques and/or systems are provided for populating and/or animating a spatial visualization, such as a map, a timeline, and/or other 2D and/or 3D visual representations of locations. The spatial visualization may be populated with events extracted from a data source (e.g., real-time events, news events, social network events, etc.), and may include relationships between events (e.g., based upon time, location, contextual similarity (e.g., social network check-in events at a restaurant), events referencing one another (e.g., an article describing a first event may comprise a hyperlink to an article describing a second event) etc.). Filter criteria (e.g., date, event type, location, etc.) may be applied to events and/or relationships when populating the spatial visualization. A sequence of events and corresponding relationships may be animated within the spatial visualization (e.g., as the events unfold over a (user) designated period of time).

Population and/or animation of spatial visualization(s)

In one embodiment, a method includes receiving a search query, determining if a bloom filter indicates an n-gram of the query does not exist in a set of object names associated with a vertical, identifying variant-tokens for each n-gram that does not exist in the set of object names, generating unique combinations of the n-grams and variant-tokens, where each unique combination includes a variant-token corresponding to each n-gram that does not exist in the set of object names for the n-gram, calculating a confidence score for each unique combination based at least in part on the search query and whether the unique combination exists in the set of object names, identifying objects matching each unique combination, where the unique combination has a confidence score greater than a threshold confidence score, and sending a search-results page responsive to the search query to the client device of the user.

Architecture that provides fully automatic generation of a geo-ontology and does not use pre-existing geo-ontologies or other location entity repositories (e.g., a licensed location). The architecture extracts the formal administrative structure of a geographical region of interest (e.g., country) (a geo-ontology of locations with attributes and relations) from a collection of entities with spatial attributes, extracts the informal administrative structure of a geographical region of interest (e.g., country) (informal administrative regions and names and informal neighborhoods and their attributes), and extracts location static rank features for all these entities (attributes used for ranking locations from the geo-ontology that appear in user queries).

Growing community of inventors

London, United Kingdom

Daniel Bernhardt