Abstract:
Particular embodiments may store, at a client device, information associated with nodes and edges of a social graph. A node may comprise a user node or a concept node. Each node may be connected by edges to other nodes of the social graph. A first user may be associated with a first user node of the social graph. The client device may receive a character string from the first user, and identify, as the first user inputs the character string, an edge-type based on the character string and one or more edges of the identified edge-type, wherein the edges are locally stored on the client device. The client device may display one or more predictive typeahead results based on the identified edge-type and the identified edges. The predictive typeahead results may correspond to nodes stored locally on the client device.
Abstract:
In one embodiment, a method includes accessing a social graph that includes user nodes and edges connecting the user nodes; identifying, based on the social graph, a set of second users corresponding to second-user nodes that are within a specified social degree of separation from a first-user node corresponding to a first user; determining, based on the social graph, that a particular feature is enabled on computing devices associated with at least a threshold number of the identified set of second users; and enabling the particular feature on a computing device associated with the first user.
Abstract:
In particular embodiments, one or more images associated with a primary user are received. The image(s) may comprise single images, a series of related images, or video frames. In each image, one or more faces are detected and/or tracked. For each face, a set of one or more candidates are selected who may be identified with the face. The primary user has a computed measure of affinity for candidates in the set through a social network, or the candidate in the set is otherwise known to the primary user. A facial recognition score is calculated for each candidate. A subset of candidates is selected, wherein each candidate in the subset has a facial recognition score above a predetermined threshold. A candidate score is calculated for each candidate based on the facial recognition score and the computed measure of affinity. A winning candidate is selected based on the candidate scores.
Abstract:
In one embodiment, a computing device receives input from a user participating in a message session. The computing device detects an emoticon in the received input and identifies an image corresponding to the emoticon. The computing device accesses the image corresponding to the emoticon and replaces the emoticon with the image in the message session.
Abstract:
In one embodiment, collecting a plurality of words from texts submitted by one or more users; for each of a plurality of communication categories, determining a usage frequency of each of one or more of the words within the communication category based on the texts; and constructing one or more customized dictionaries that each comprise a different blending of selected words.
Abstract:
Particular embodiments determine that a textual term is not associated with a known meaning. The textual term may be related to one or more users of the social-networking system. A determination is made as to whether the textual term should be added to a glossary. If so, then the textual term is added to the glossary. Information related to one or more textual terms in the glossary is provided to enhance auto-correction, provide predictive text input suggestions, or augment social graph data. Particular embodiments discover new textual terms by mining information, wherein the information was received from one or more users of the social-networking system, was generated for one or more users of the social-networking system, is marked as being associated with one or more users of the social-networking system, or includes an identifier for each of one or more users of the social-networking system.
Abstract:
In one embodiment, the method includes a computing device receiving one or more characters as a user enters the characters into a graphical user interface (GUI) of the computing device. The method also includes the computing device determining one or more auto-suggestions, where each of the auto-suggestions presents a character string determined based at least in part on the entered characters. The method also includes the computing device determining a degree of difficulty of the user completing the respective character string for each of the auto-suggestions. The method further includes, for each of the auto-suggestions, if the degree of difficulty is at least approximately equal to or exceeds a pre-determined threshold, the computing device providing to the user the auto-suggestion for completing the character string.
Abstract:
In one embodiment, a method includes receiving data uniquely identifying a particular user to the verification authority and a request to access a shared device. The shared device being configured for use by at least a number of users. The method also includes accessing a social graph of the particular user to determine whether one or more users in the social graph have previously accessed the shared device; and displaying on a display of the mobile device information indicating which of the users in the social graph have previously accessed the shared device.
Abstract:
In one embodiment, user information for a user of a social-networking system is retrieved. Device information is determined for a device associated with the user. Based on the device information or the user information, content associated with the user is retrieved. Using the retrieved content, a content board is composed for use in a cover feed displayed on the device The content board may comprise a background image. Finally, the content board is sent to the device. In one embodiment, updated information for content associated with the user is retrieved. The updated information may be associated with content that was included in a previously-provided content board. Using the updated information, an update to the previously-provided content boards is composed. Finally, the updates may be sent to the previously-provided content boards to the device.
Abstract:
In one embodiment, the method includes receiving one or more characters inputted by a user; calculating a degree of difficulty of a character string, wherein the degree of difficulty is based on the characters inputted by the user, and wherein the characters inputted by the user comprise a portion of the character string; and if the degree of difficulty is equal to or exceeds a pre-determined threshold, then presenting, on a display of the computing device, an auto-suggestion for completing the character string; else, not presenting, on the display of the computing device, the auto-suggestion for completing the character string.