Abstract: Machine learning models are used for ranking news feed stories presented to users of a social networking system. The social networking system divides its users into different sets, for example, based on demographic characteristics of the users and generates one model for each set of users. The models are periodically retrained. The news feed ranking model may rank news feeds for a user based on information describing other users connected to the user in the social networking system. Information describing other users connected to the user includes interactions of the other users with objects associated with news feed stories. These interactions include commenting on a news feed story, liking a news feed story, or retrieving information, for example, images, videos associated with a news feed story.

Abstract: A method for finding similar search terms (word matching) is provided. The method receives a query of at least a first word and automatically applies the first word to a vocabulary amplifier. The vocabulary amplifier retrieves an associated word for the first word and accesses one or more database sources to retrieve an associated word. Each associated word is presented to an output interface. The amplifier then receives information from an input interface for the associated word and classifies the associated word based upon the received characterizing information.