Abstract: In one embodiment, a method includes scoring a set of content objects based on one or more user-engagement factors, identifying one or more related content objects, wherein each related content objects is connected within the online social network to one or more content objects of the set of content objects having a score greater than a threshold score, generating a plurality of structured queries that each comprise references to one or more content objects, wherein at least one of the structured queries is a personalized query comprising a reference to at least one of the related content objects, and sending instructions to a client device for presenting one or more of the generated structured queries to a first user for display on an interface currently accessed by the first user, wherein at least one of the sent structured queries is a personalized query.

Abstract: In one embodiment, a method includes receiving, from a client system of a first user, a text query including one or more n-grams, including an ambiguous n-gram. The method includes searching multiple keyword generators to identify one or more keyword suggestions matching the ambiguous n-gram, each keyword generator having a particular type. The method includes calculating, by a particular scoring algorithm for each keyword generator, a keyword score for each identified keyword suggestion. The scoring algorithm includes multiple weighting factors chosen based on the type of the keyword generator. The method includes generating a set of suggested queries including at least a portion of the text query and one or more identified keyword suggestions and filtering suggested queries from the set based on privacy settings associated with the identified keyword suggestions. The method includes sending, to the client system, instructions for presenting one or more of the suggested queries.

Abstract: In one embodiment, a method includes receiving unstructured text from a user of a social-networking system, determining whether the unstructured text includes a request for a recommendation, identifying one or more first entity names in the unstructured text, generating a structured query based upon the one or more first entity names, identifying, in the social graph, one or more second entity names corresponding to the structured query, and presenting the one or more second entity names and the unstructured text in a social context of the user. The unstructured text may include text of a post or message generated by the user on a social-networking system. A score may be generated based on the unstructured text to determine whether the text includes a request for recommendation using a machine-learning model based on comparison of the unstructured text to the one or more predetermined words associated with requests for recommendation.

Abstract: In one embodiment, a method includes accessing a post of an online social network, extracting keywords from the content of the first post and the metadata associated with the first post, determining topics associated with each extracted keyword, calculating a topic-score based on a relevance of the topic to the post for each topic, generating a suggested keyword query corresponding to the post, the suggested keyword query comprising extracted keywords corresponding to each topic having a topic-score greater than a threshold topic-score, and sending the post and the suggested keyword query to a client device of a first user for display.

Abstract: In one embodiment, a method includes receiving, from a client system of a first user, a text query including one or more n-grams, including an ambiguous n-gram. The method includes searching multiple keyword generators to identify one or more keyword suggestions matching the ambiguous n-gram, each keyword generator having a particular type. The method includes calculating, by a particular scoring algorithm for each keyword generator, a keyword score for each identified keyword suggestion. The scoring algorithm includes multiple weighting factors chosen based on the type of the keyword generator. The method includes generating a set of suggested queries including at least a portion of the text query and one or more identified keyword suggestions and filtering suggested queries from the set based on privacy settings associated with the identified keyword suggestions. The method includes sending, to the client system, instructions for presenting one or more of the suggested queries.

Abstract: In one embodiment, a method includes receiving from a client system of a first user an unstructured text query. The method includes parsing the text query to identify one or more n-grams. At least one of the n-grams is an ambiguous n-gram. The method includes searching a plurality of keyword generators to identify one or more keyword suggestions matching the ambiguous n-gram. The method further includes calculating a keyword score for each identified keyword suggestions and generating one or more suggested queries including one or more n-grams identified from the text query. The one or more identified keyword suggestions having a keyword score greater than a threshold keyword score. The method includes sending one or more of the suggested queries to the client system of the first user for display.

Abstract: In one embodiment, a method includes scoring a set of content objects based on one or more user-engagement factors, identifying one or more related content objects, wherein each related content objects is connected within the online social network to one or more content objects of the set of content objects having a score greater than a threshold score, generating a plurality of structured queries that each comprise references to one or more content objects, wherein at least one of the structured queries is a personalized query comprising a reference to at least one of the related content objects, and sending instructions to a client device for presenting one or more of the generated structured queries to a first user for display on an interface currently accessed by the first user, wherein at least one of the sent structured queries is a personalized query.

Abstract: In one embodiment, a method includes accessing a social graph that includes a plurality of nodes and edges. A first node corresponds to a first user associated with an online social network and second nodes correspond to a concept or a second user. The method further comprises scoring a first set of nodes of the second nodes based on user-engagement factors. The method further comprises identifying common nodes that are connected by edges to nodes of the first set of nodes that have a score greater than a threshold score. The method further comprises generating structured queries and sending the structured queries to the user, the sent structured queries being a personalized query.

Abstract: In one embodiment, a method includes receiving a text query to search for news-posts of the online social network. The method includes parsing the text query to identify one or more n-grams. The method includes searching an index of keyword phrases to identify one or more keyword phrases matching one or more of the n-grams of the text query. Each of the identified keyword phrases is news-related. The method includes calculating a news-score for each of the identified keyword phrases. The method includes generating one or more suggested queries. Each suggested query includes one or more n-grams identified form the text query and one or more identified keyword phrases having a news-score greater than a threshold news-score. The method includes sending one or more of the suggested queries to search for news-posts of the online social network.

Abstract: In one embodiment, a method includes accessing a post of an online social network, extracting keywords from the content of the first post and the metadata associated with the first post, determining topics associated with each extracted keyword, calculating a topic-score based on a relevance of the topic to the post for each topic, generating a suggested keyword query corresponding to the post, the suggested keyword query comprising extracted keywords corresponding to each topic having a topic-score greater than a threshold topic-score, and sending the post and the suggested keyword query to a client device of a first user for display.

Abstract: In one embodiment, a method includes receiving from a client system of a first user an unstructured text query. The method includes parsing the text query to identify one or more n-grams. At least one of the n-grams is an ambiguous n-gram. The method includes searching a plurality of keyword generators to identify one or more keyword suggestions matching the ambiguous n-gram. The method further includes calculating a keyword score for each identified keyword suggestions and generating one or more suggested queries including one or more n-grams identified from the text query. The one or more identified keyword suggestions having a keyword score greater than a threshold keyword score. The method includes sending one or more of the suggested queries to the client system of the first user for display.

Abstract: In one embodiment, a method includes receiving unstructured text from a user of a social-networking system, determining whether the unstructured text includes a request for a recommendation, identifying one or more first entity names in the unstructured text, generating a structured query based upon the one or more first entity names, identifying, in the social graph, one or more second entity names corresponding to the structured query, and presenting the one or more second entity names and the unstructured text in a social context of the user. The unstructured text may include text of a post or message generated by the user on a social-networking system. A score may be generated based on the unstructured text to determine whether the text includes a request for recommendation using a machine-learning model based on comparison of the unstructured text to the one or more predetermined words associated with requests for recommendation.

Abstract: In one embodiment, a method includes accessing a social graph that includes a plurality of nodes and edges. A first node corresponds to a first user associated with an online social network and second nodes correspond to a concept or a second user. The method further comprises scoring a first set of nodes of the second nodes based on user-engagement factors. The method further comprises identifying common nodes that are connected by edges to nodes of the first set of nodes that have a score greater than a threshold score. The method further comprises generating structured queries and sending the structured queries to the user, the sent structured queries being a personalized query.