Abstract: In one embodiment, a method may include, by one or more computing systems of an online social network, receiving a first event listing corresponding to a first event, placing the first event listing in a first bucket of an array based on a time of the first event, identifying one or more second event listings having a time placing them in the first bucket of the array or one of the buckets of the array adjacent to the first bucket, computing, for each of the second event listings, a similarity score between the first event listing and the second event listing, and updating each second event listing having a similarity score greater than a threshold score to include redirection information to the first event listing.

Abstract: In one embodiment, a method may include, by one or more computing systems of an online social network, receiving a first event listing corresponding to a first event, placing the first event listing in a first bucket of an array based on a time of the first event, identifying one or more second event listings having a time placing them in the first bucket of the array or one of the buckets of the array adjacent to the first bucket, computing, for each of the second event listings, a similarity score between the first event listing and the second event listing, and updating each second event listing having a similarity score greater than a threshold score to include redirection information to the first event listing.

Abstract: A social networking system allows its users to provide metadata for association with objects maintained by the social networking system. Some of the metadata may be presented to other users along with the object. To prevent association of inaccurate metadata with an object, the social networking system determines a confidence value associated with a user who provided metadata for association with an object. The confidence value indicates of the accuracy of metadata provided by the user relative to information associated with the object. If the user has previously provided less than a threshold amount of metadata to associate with objects, the confidence score is determined using characteristics associated with the user by the social networking system. The metadata provided by the user is stored but not associated with the object if the user's confidence value is less than a threshold value.

Abstract: A social networking system allows its users to provide metadata for association with objects maintained by the social networking system. Some of the metadata may be presented to other users along with the object. To prevent association of inaccurate metadata with an object, the social networking system determines a confidence value associated with a user who provided metadata for association with an object. The confidence value indicates of the accuracy of metadata provided by the user relative to information associated with the object. If the user has previously provided less than a threshold amount of metadata to associate with objects, the confidence score is determined using characteristics associated with the user by the social networking system. The metadata provided by the user is stored but not associated with the object if the user's confidence value is less than a threshold value.

Abstract: A system and method is provided for recognizing a pattern in queries and using the recognized pattern to categorize queries under existing query categories. An entity text that represents a real-world object is detected in a query. The entity text is categorized into an entity category. The query is then rewritten as an annotated query by replacing the entity text with a placeholder for any entity matching the entity category. The annotated query is compared to other queries that are associated with query categories to find a query category that is frequently associated with the annotated query. The query category frequently associated with the annotated query is used to generate a rule. The rule indicates that any query matching the annotated query is likely to fit into the query category. The rule can be used to assign categories to queries or correct incorrectly categorized queries.

Abstract: A system and method is provided for rewriting a query sent from a user to a search provider. The search provider displays results from content providers through modules associated with the content providers. The search provider predicts whether the query would be successful for one or more modules using information about keywords that have been tested on the module. The search provider attempts to replace a query predicted to not be successful for the module by searching for the query in a list of aliases. Each list in the list of aliases is associated with an object identifier. Each object identifier identifies a real-world object or entity to which the object identifier refers. If the query is found in a list of aliases, the search provider selects another keyword from the list. The search provider sends the selected keyword, instead of the query, to the module.

Abstract: A method and apparatus for disambiguating a word or phrase is provided. Keywords are detected in a text. The keywords are each associated with one or more objects, and the objects are each categorized into one or more categories. Correlation values are retrieved from a correlation matrix to determine the frequency with which the categories co-occur. Based on the correlation values, a first category and a second category are selected for a first keyword and a second keyword. A first object associated with the first category can then be selected as the likely meaning for the first keyword. A second object associated with the second category can then be selected as the likely meaning for the second keyword. Content is sent to the client based on any of the first keyword, the first object, the first category, the second keyword, the second object, and the second category.

Abstract: A system and method is provided for recognizing a pattern in queries and using the recognized pattern to categorize queries under existing query categories. An entity text that represents a real-world object is detected in a query. The entity text is categorized into an entity category. The query is then rewritten as an annotated query by replacing the entity text with a placeholder for any entity matching the entity category. The annotated query is compared to other queries that are associated with query categories to find a query category that is frequently associated with the annotated query. The query category frequently associated with the annotated query is used to generate a rule. The rule indicates that any query matching the annotated query is likely to fit into the query category. The rule can be used to assign categories to queries or correct incorrectly categorized queries.

Abstract: A method and apparatus for establishing a degree of confidence that a real world object correctly represents a string is provided. The method includes receiving a string in a context. The string can then be related to a real world object with a degree of confidence based on a click log, link graph, redirect list, or object list. The object can be initially associated with a category or mapped to a category. Then, the degree of confidence can be raised if the category matches the context. If the category does not match the context, the degree of confidence can be lowered. An online service provider using the method can then determine what type of content to send a user based on the confidence level.

Abstract: A classifier using a logistic regression technique permits previously acquired search results to be used to perform a new search. A user inputs search terms and queries a database of previous search results. A logistical regression calculation is performed using sets of data such that the time execution performance is at least a factor of 10 improvement over a conventional technique. In our experiments where real world data was used, the execution time was reduced up to 353 times as compared to the conventional technique. The Iteratively Reweighted Least Squares (IRLS) method is used for the logistical regression method and beta vector values are calculated from the database data set. A vector of the user input terms is multiplied by the beta vector values to produce an ordered list of documents satisfying the user search terms.

Abstract: A classifier using a logistic regression technique permits previously acquired search results to be used to perform a new search. A user inputs search terms and queries a database of previous search results. A logistical regression calculation is performed using sets of data such that the time execution performance is at least a factor of 10 improvement over a conventional technique. In our experiments where real world data was used, the execution time was reduced up to 353 times as compared to the conventional technique. The Iteratively Reweighted Least Squares (IRLS) method is used for the logistical regression method and beta vector values are calculated from the database data set. A vector of the user input terms is multiplied by the beta vector values to produce an ordered list of documents satisfying the user search terms.