Patent application number | Description | Published |
20110196855 | REAL TIME CONTENT SEARCHING IN SOCIAL NETWORK - Indexing and retrieving real time content in a social networking system is disclosed. A user-term index includes user-term partitions, each user-term partition comprising temporal databases. As a post is received from a user, a user identifier, a post identifier, and a post is extracted. An object store communicatively coupled to a temporal database for recently received content is queried to determine whether terms in the post has already been stored. A term identifier is stored in the user-term index with the user and post identifiers. A forward index stores the post by post identifier. Responsive to a search query, the user-term index is searched by the user's connections and the terms. A real time search engine compiles the results of the user-term index query and retrieves the stored posts from the forward index. The search results may then be ranked and cached before presentation to the searching user. | 08-11-2011 |
20130173611 | GENERATION OF NICKNAME DICTIONARY - Methods, apparatuses and systems for generating a name-word dictionary that includes associations between names of users and candidate words (e.g., nicknames) based on statistical analysis of user communications observed at a network communications facility, such as a social network system, an email provider and the like. | 07-04-2013 |
20130246390 | REAL TIME CONTENT SEARCHING IN SOCIAL NETWORK - Indexing and retrieving real time content in a social networking system is disclosed. A user-term index includes user-term partitions, each user-term partition comprising temporal databases. As a post is received from a user, a user identifier, a post identifier, and a post is extracted. An object store communicatively coupled to a temporal database for recently received content is queried to determine whether terms in the post has already been stored. A term identifier is stored in the user-term index with the user and post identifiers. A forward index stores the post by post identifier. Responsive to a search query, the user-term index is searched by the user's connections and the terms. A real time search engine compiles the results of the user-term index query and retrieves the stored posts from the forward index. The search results may then be ranked and cached before presentation to the searching user. | 09-19-2013 |
20140052540 | PROVIDING CONTENT USING INFERRED TOPICS EXTRACTED FROM COMMUNICATIONS IN A SOCIAL NETWORKING SYSTEM - A social networking system may infer interests based on extracted topics from content items on the social networking system. A user's comments and page likes in a social networking system are used to infer topics in which the user is interested. Topics may also be automatically extracted from users' posts, and the extracted topics may be generalized using a category tree to identify additional topics for the user. The social networking system may target content such as advertisements to users based on these extracted topics. For example, the social networking system may boost stories related to the extracted topics in the user's content feeds, append stories about the extracted topics to advertisements that are also related to the topics, append advertisements to stories about the extracted topics, or use the extracted topics as targeting criteria for an advertisement. | 02-20-2014 |
20140156566 | CUSTOMIZED PREDICTORS FOR USER ACTIONS IN AN ONLINE SYSTEM - Online systems generate predictors for predicting actions of users of the online system. The online system receives requests to generate predictor models for predicting whether a user is likely to take an action of a particular action type. The request specifies the type of action and criteria for identifying a successful instance of the action type and a failure instance of the action type. The online system collects data including successful and failure instances of the action type. The online system generates one or more predictors of different types using the generated data. The online system evaluates and compares the performance of the different predictors generated and selects a predictor based on the performance. The online system returns a handle to access the generated predictor to the requester of the predictor. | 06-05-2014 |
20140156637 | QUERYING FEATURES BASED ON USER ACTIONS IN ONLINE SYSTEMS - Online systems, for example, social networking systems store features describing relations between entities represented in the online system. The information describing the features is represented as a graph. The online system maintains a cumulative feature graph and an incremental feature graph. Feature values based on recent user actions are stored in the incremental graph and feature values based on previous actions are stored in the cumulative graph. Periodically, the information stored in the incremental feature graph is merged with the information stored in the cumulative feature graph. The incremental graph is marked as inactive during the merge and information based on new user actions is stored in an active incremental feature graph. If a request for feature information is received, the feature information obtained from the cumulative feature graph, inactive incremental feature graph and the active incremental feature graph are combined to determine the feature information. | 06-05-2014 |
20140156744 | UPDATING FEATURES BASED ON USER ACTIONS IN ONLINE SYSTEMS - Online systems, for example, social networking systems store features describing relations between entities represented in the online system. The information describing the features is represented as a graph. The online system maintains a cumulative feature graph and an incremental feature graph. Feature values based on recent user actions are stored in the incremental graph and feature values based on previous actions are stored in the cumulative graph. Periodically, the information stored in the incremental feature graph is merged with the information stored in the cumulative feature graph. The incremental graph is marked as inactive during the merge and information based on new user actions is stored in an active incremental feature graph. If a request for feature information is received, the feature information obtained from the cumulative feature graph, inactive incremental feature graph and the active incremental feature graph are combined to determine the feature information. | 06-05-2014 |
20140156745 | DISTRIBUTING USER INFORMATION ACROSS REPLICATED SERVERS - Online systems store information describing a large number of users in order to process requests accessing the user information. The user information is distributed across multiple servers. The distribution is performed so that the information is available even if one or more servers fail. The user information is distributed across a first set of servers and a second copy of the user information is distributed across a second set of servers. The user information from each server of the first set is uniformly distributed across multiple servers from the second set, for example, using random distribution, round robin strategy, or any other strategy that uniformly distributes the information across a given set of processors. Requests previously directed to a failed server are redistributed across multiple servers thereby load balancing the processing of these requests. | 06-05-2014 |
20140214545 | RANKING OF ADVERTISEMENTS FOR DISPLAY ON A MOBILE DEVICE - An online system ranks an advertisement for display on a user's mobile device based on a mobile friendliness score for the advertisement. The mobile friendliness score is calculated using information about the advertisement's landing page, such as feedback received for the landing page. The mobile friendliness score may also reflect additional information, such as information about the user to be presented with the advertisement, parameters of the mobile device on which the advertisement is to be presented, or user feedback associated with the advertisement. Based on the mobile friendliness score, the advertisement is ranked for presentation to the user via the mobile device. A higher ranking may increase the likelihood and/or frequency of presenting the advertisement via the user's mobile device. | 07-31-2014 |