Abstract: A low-latency database analysis system using an object index may include obtaining data expressing a usage intent, and, in response to a determination that the data expressing the usage intent includes object search request data including a user identifier and zero or more object search terms, obtaining, from an object-index responsive to the object search request data, object indexing data for an object, obtaining object detail data for the object, obtaining an object visualization capture for the object, and outputting object search response data including the object visualization capture and at least a portion of the object detail data for presentation to a user.

Abstract: Operating a low-latency database analysis system with phrase translation may include obtaining a locale-specific phrase localization rule and a canonical phrase localization rule for a phrase, generating a locale-specific index and a locale-specific finite state machine for the locale using the localization definition data and a canonical finite state machine, generating a resolved-request by obtaining a locale-specific token representing locale-specific input data by traversing the locale-specific index, obtaining a canonical token associated with locale-specific token, obtaining a locale-specific phrase by traversing the locale-specific finite state machine, obtaining a canonical phrase corresponding to the locale-specific phrase, the canonical phrase including the canonical token, generate a data-query based on the canonical phrase, obtaining results data responsive to the data expressing the usage intent by executing a query corresponding to the data-query by an in-memory database of the low-latency datab

Abstract: An online system generates, based on previously recorded content presentations, user value distributions for users of the online system. The online system also receives third party specifications from a third party system. Using this information, the online system generates a simulation for presenting content to users of the online system based on the third party specifications. For each iteration of the simulation, the online system randomly selects a user of the online system matching specifications, accesses the user value distribution for the randomly selected user, computes a selection probability of a successful content presentation to the randomly selected user, and simulates a content transmission based on the selection probability. The results of the simulation are reported to the third party system.

Abstract: An online system organizes content items based on assignments of the content items to categories within a hierarchical taxonomy. The online system tracks the progress of each online system user towards frequency caps applied to the categories. A user who reaches a frequency cap applied to a category is included in an exclusion group associated with the category, making them ineligible to be presented with content items associated with the category. Upon identifying an opportunity to present content to a viewing user, the online system determines whether the user is included in an exclusion group associated with a category. If not, the online system may present a content item associated with the category to the user, update the user's progress towards a frequency cap applied to the category, and determine whether to include the viewing user in the exclusion group based on the updated progress and the frequency cap.

Abstract: An online system generates, based on previously recorded content presentations, user value distributions for users of the online system. The online system also receives third party specifications from a third party system. Using this information, the online system generates a simulation for presenting content to users of the online system based on the third party specifications. For each iteration of the simulation, the online system randomly selects a user of the online system matching specifications, accesses the user value distribution for the randomly selected user, computes a selection probability of a successful content presentation to the randomly selected user, and simulates a content transmission based on the selection probability. The results of the simulation are reported to the third party system.

Abstract: An online system presents ads on behalf of advertisers to users of the online system. For an ad campaign, the online system determines bid prices to be associated with an ad for different eligible users based at least on user cost models associated with the eligible users and a value curve that specifies an amount of value the advertiser derives from each ad impression. Using user cost models and the value curve, the online system evaluates how much value an advertiser will derive from ad impressions. The online system maximizes an expected value that an advertiser can derive from ad impressions to an eligible user to determine a bid price. The online system calculates an expected value as an amount of value that the advertiser derives from the ad impression with a bid price weighted by a likelihood of winning auctions with a bid price.

Abstract: An online system determines an advertiser value curve specific to a content item that may be presented to a viewing user of the online system, where points along the advertiser value curve represent values of potential impressions that may be obtained for the content item, which decreases as the number of potential impressions increases. The online system also determines a user cost curve specific to the viewing user, where points along the user cost curve represent costs of previous impressions of one or more content items obtained for the viewing user, which increases as the number of impressions of the content items increases. An efficient bid amount for each impression opportunity for the viewing user to view the content item is determined based on a number of total impressions and a bid amount that are associated with an intersection of the curves.

Abstract: An online system manages a set of custom features for a third party system stored in user profiles. The online system accesses predictors for the third party system based on the set of custom features for the third party system, the predictors generating predictions for users to the third party system based on the custom features of a lifetime expected incremental value to the third party system from presenting the sponsored content item to the target user. The online system receives from the third party system, data elements for a target user, the data elements related to the actions performed by the target user. The online system extracts custom features from the data elements based on a custom feature definition associated with the third party system. The online system determines a value score for the target user based on the extracted custom features for the target user using the predictors.