Abstract: In one embodiment, a set of user groups of a social-networking system may be accessed. A first subset of the user groups may be determined for a particular user of the social-networking system, based on one or more filtering criteria. A number of recommendation-source processes may be applied to the first subset to determine a number of second subsets of the first set. Each recommendation-source process may represent a particular recommendation source. The second subsets may be combined into a list of user groups. The list of user groups may be ranked, and sent to the particular user.

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 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 set of user groups of a social-networking system may be accessed. A first subset of the user groups may be determined for a particular user of the social-networking system, based on one or more filtering criteria. A number of recommendation-source processes may be applied to the first subset to determine a number of second subsets of the first set. Each recommendation-source process may represent a particular recommendation source. The second subsets may be combined into a list of user groups. The list of user groups may be ranked, and sent to the particular user.