Abstract: According to examples, a system for generating and delivering enhanced content utilizing remote rendering and data streaming is described. The system may include a processor and a memory storing instructions. The processor, when executing the instructions, may cause the system to transmit a selected engagement content item for transmission to a user device and receive an indication of interest relating to the selected engagement content item. The processor, when executing the instructions, may then select, based on the received indication of interest, an enhanced content item, process the enhanced to content item to generate a stream of content data associated with the enhanced content item and transmit the stream of content data associated with the enhanced content item to the user device.

Abstract: According to examples, a system for generating and delivering enhanced content utilizing remote rendering and data streaming is described. The system may include a processor and a memory storing instructions. The processor, when executing the instructions, may cause the system to transmit a selected engagement content item for transmission to a user device and receive an indication of interest relating to the selected engagement content item. The processor, when executing the instructions, may then select, based on the received indication of interest, an enhanced content item, process the enhanced to content item to generate a stream of content data associated with the enhanced content item and transmit the stream of content data associated with the enhanced content item to the user device.

Abstract: In one embodiment, a method includes receiving a request to identify a similarity in public sentiments for each pair from a plurality of entities from a second computing device, where the request includes names of the plurality of entities, accessing a table of word vector relationships, where the table of word vector relationships includes a plurality of unique n-grams and their corresponding word vectors, and where each of the word vectors represents a semantic context of a corresponding n-gram as a point in a d-dimensional embedding space, looking up word vectors corresponding to each of the names using the table, calculating, for each of the word vectors, a similarity metric to each of the word vectors, and sending a response message to the second computing device, where the response message includes calculated similarity metrics corresponding to all the pairs of the word vectors.

Abstract: In one embodiment, a method includes receiving a request to identify a word representing a target concept that is in a first relationship with a particular concept such that the first relationship is analogous to a second relationship in which a first reference concept is with a second reference concept, accessing a table of word vector relationships, looking up a particular word vector, a first reference word vector, and a second reference word vector, determining an imaginary vector such that a first vector from the first reference word vector to the second reference word vector is equal to a second vector from the particular word vector to the imaginary vector, selecting a target word vector closest to the imaginary vector, identifying a target n-gram corresponding to the target word vector, and sending a response message comprising the target n-gram.

Abstract: In one embodiment, a method includes receiving a request to generate k keywords each of which is semantically related to a particular subject, where the request includes an input n-gram representing the particular subject, accessing a table of word vector relationships, where the table includes a plurality of unique n-grams and their corresponding word vectors, and wherein each of the word vectors represents a semantic context of a corresponding n-gram as a point in a d-dimensional embedding space, looking up, using the table, a first word vector corresponding to the input n-gram, selecting k word vectors closest to the first word vector in the embedding space using the table and based on a similarity metric, identifying, for each of the selected word vectors, a corresponding n-gram by looking up the selected word vector in the table, and sending a response message including the identified n-grams.

Abstract: In one embodiment, a method includes receiving a request to identify public sentiments for one or more entities from a second computing device, where each of the attribute n-grams represents an attribute among a plurality of pre-determined attributes, accessing a table of word vector relationships, looking up entity word vectors corresponding to each of the names for the one or more entities and attribute word vectors corresponding to each of the plurality of attribute n-grams using the table, calculating, for each of the entity word vectors, a similarity metric to each of the attribute word vectors, and sending a response message to the second computing device, where the response message includes calculated similarity metrics corresponding to all the pairs of an entity word vector and an attribute word vector.

Abstract: In one embodiment, a method includes receiving a request to generate a visualization of public sentiments regarding a particular subject by a plurality of clusters, where each cluster includes a plurality of words semantically close to each other, constructing a first corpus of text by collecting text containing the input n-gram from a plurality of user-created content objects in the online social network, identifying a list of unique n-grams appearing in the first corpus of text, generating a table comprising unique n-grams in the list and their corresponding word vectors using a word embedding model, classifying word vectors in the table into a plurality of clusters based on semantic similarities of the word vectors, and sending, as a response to the request, instructions to display n-grams in the table in a two-dimensional display space, where n-grams corresponding to word vectors that belong to a cluster are displayed together.

Abstract: In one embodiment, a method includes receiving a request to generate k words that each approximates a representation of a relationship between two concepts, where the request includes two input n-grams that each represent one of the two concepts, accessing a table of word vector relationships, where the table includes a plurality of unique n-grams and their corresponding word vectors, looking up word vectors corresponding to each of the two input n-grams using the table, calculating an average vector of the word vectors corresponding to the two input n-grams, selecting, using the table and based on a similarity metric, k word vectors closest to the average vector in the embedding space, identifying, for each of the selected word vectors, a corresponding n-gram by looking up the selected word vector in the table, and sending a response message, the response message comprising the identified n-grams.

Abstract: In one embodiment, a method includes receiving a request to generate a two-dimensional visualization of public sentiments regarding a particular subject, where the request includes an input n-gram representing the particular subject, constructing a first corpus of text by collecting text containing the input n-gram from a plurality of user-created content objects in the online social network, identifying a list of unique n-grams appearing in the first corpus of text, generating a table comprising unique n-grams in the list and their corresponding word vectors using a word embedding model, condensing the d-dimensional word vectors in the table into a two-dimensional word vectors; and sending, as a response to the request, instructions to display n-grams in the table on a two-dimensional display space, where each n-gram is placed at a location of the corresponding condensed word vector.

Abstract: An online system includes a method for identifying content to provide to a user of the online system. The online system receives a request to present content to users of the online system. The request may include a hierarchy of moments, wherein each moment of the hierarchy of moments is associated with a content creative. The online system identifies an impression opportunity to present the content to a user of the online system. The online system identifies a set of candidate moments occurring for the user from the hierarchy of moments. The online system selects a moment from the set of candidate moments based on the hierarchy of moments. The online system finally identifies the content creative associated with the identified moment, and then presents the content creative to the user.

Abstract: An online system receives information from an entity identifying a set of users of the online system and groups users included in the set into clusters based on their similarities using a clustering model or algorithm (e.g., k-means clustering) and based on one or more parameters specified by the entity. The online system generates expanded clusters that include additional users in one or more clusters based on similarities between the additional users and users in various clusters. If an additional user is included in multiple expanded clusters, the online assigns the additional user exclusively to an expanded cluster that best fits the user.