Abstract: A function creation method is disclosed. The method comprises defining one or more database function inputs, defining cluster processing information, defining a deep learning model, and defining one or more database function outputs. A database function is created based at least in part on the one or more database function inputs, the cluster set-up information, the deep learning model, and the one or more database function outputs. In some embodiments, the database function enables a non-technical user to utilize deep learning models.

Abstract: A function creation method is disclosed. The method comprises defining one or more database function inputs, defining cluster processing information, defining a deep learning model, and defining one or more database function outputs. A database function is created based at least in part on the one or more database function inputs, the cluster set-up information, the deep learning model, and the one or more database function outputs. In some embodiments, the database function enables a non-technical user to utilize deep learning models.

Abstract: An online system identifies seed users of high value to a sponsored content provider. Characteristics of the seed users are identified, and additional users having a threshold measure of similarity to the seed users are identified based on the characteristics. A score is determined for each of the additional users based on the measure of similarity. The seed users are placed in an initial tier of a tiered set of users for the sponsored content, and the additional users are placed in additional tiers of the tiered set of users based upon the determined scores such that each additional tier includes those users of the additional users having a specified range of determined scores, the tiers of the tiered set of users ranked according to the determined scores of users within each tier.

Abstract: A social networking system presents users with a content items and ad requests, which may include targeting criteria specifying a topic. Interactions by users who were presented with an advertisement from an ad request including targeting criteria specifying the topic are stored by the social networking system and used to identify a cluster group of additional users having characteristics similar to characteristics of users who were presented with the advertisement from the ad request including targeting criteria specifying the topic and who interacted with the advertisement. The social networking system determines scores for additional users in the cluster group based on measures of similarity between the additional users and the users who were presented with the advertisement and who interacted with the advertisement. Based on the determined scores, the social networking system associates additional users in the cluster group with the topic.

Abstract: A social networking system presents users with a content items and ad requests, which may include targeting criteria specifying a topic. Interactions by users who were presented with an advertisement from an ad request including targeting criteria specifying the topic are stored by the social networking system and used to identify a cluster group of additional users having characteristics similar to characteristics of users who were presented with the advertisement from the ad request including targeting criteria specifying the topic and who interacted with the advertisement. The social networking system determines scores for additional users in the cluster group based on measures of similarity between the additional users and the users who were presented with the advertisement and who interacted with the advertisement. Based on the determined scores, the social networking system associates additional users in the cluster group with the topic.

Abstract: A social networking system selects advertisements for a user based on user characteristics of the user in response to a request to present an advertisement to the user. To increase the number of advertisements eligible for presentation to the user, the social networking system associates the user with one or more cluster groups associated with targeting criteria that are not satisfied by the user's characteristics. To determine whether to associate a user with a cluster group, the social networking system determines a cluster score for the cluster group based on the user's characteristics. If the cluster score equals or exceeds a cluster cutoff score for the cluster group, the user is associated with the cluster group. The cluster cutoff score may be determined based on an estimated distribution of users so that a target number or percentage of users have cluster scores less than the cluster cutoff score.

Abstract: An online system identifies seed users of high value to a sponsored content provider. Characteristics of the seed users are identified, and additional users having a threshold measure of similarity to the seed users are identified based on the characteristics. A score is determined for each of the additional users based on the measure of similarity. The seed users are placed in an initial tier of a tiered set of users for the sponsored content, and the additional users are placed in additional tiers of the tiered set of users based upon the determined scores such that each additional tier includes those users of the additional users having a specified range of determined scores, the tiers of the tiered set of users ranked according to the determined scores of users within each tier.

Abstract: An online system identifies seed users of high value to a sponsored content provider. Characteristics of the seed users are identified, and additional users having a threshold measure of similarity to the seed users are identified based on the characteristics. A score is determined for each of the additional users based on the measure of similarity. The seed users are placed in an initial tier of a tiered set of users for the sponsored content, and the additional users are placed in additional tiers of the tiered set of users based upon the determined scores such that each additional tier includes those users of the additional users having a specified range of determined scores, the tiers of the tiered set of users ranked according to the determined scores of users within each tier.

Abstract: An online system receives information from an entity identifying a set of users of the online system and groups users included in the set into clusters based on their similarities using a clustering model or algorithm (e.g., k-means clustering) and based on one or more parameters specified by the entity. The online system generates expanded clusters that include additional users in one or more clusters based on similarities between the additional users and users in various clusters. If an additional user is included in multiple expanded clusters, the online assigns the additional user exclusively to an expanded cluster that best fits the user.

Abstract: A target audience for an ad campaign is determined during an exploration period of the ad campaign by modifying the target audience based on the fulfillment of performance objectives. An initial target audience may be provided by the advertiser or determined by the social networking system based on ad campaigns having similar ad content or other similar characteristics. Advertisements associated with the ad campaign are served to users of the initial target audience. A subset of the target audience that fulfills the performance objectives of the ad campaign is identified and those users are used to generate a new targeting audience to target users that “look like” the subset of the target audience. The new targeting audience is used in place of the initial target audience to improve targeting for the advertisement. This process may be iteratively performed to refine the target audience during the exploration period.

Abstract: A social networking system presents users with a content items and ad requests, which may include targeting criteria specifying a topic. Interactions by users who were presented with an advertisement from an ad request including targeting criteria specifying the topic are stored by the social networking system and used to identify a cluster group of additional users having characteristics similar to characteristics of users who were presented with the advertisement from the ad request including targeting criteria specifying the topic and who interacted with the advertisement. The social networking system determines scores for additional users in the cluster group based on measures of similarity between the additional users and the users who were presented with the advertisement and who interacted with the advertisement. Based on the determined scores, the social networking system associates additional users in the cluster group with the topic.

Abstract: A social networking system receives an advertisement request including multiple sets of targeting criteria. To increase the number of users eligible to be presented with the advertisement request, the social networking system generates a cluster group associated with each set of targeting criteria. A cluster group associated with a set of targeting criteria includes users satisfying the targeting criteria and additional users that do not satisfy the targeting criteria. The social networking system determines an amount of overlap between the cluster groups. If the amount of overlap equals or exceeds a threshold value, the social networking system combines the cluster groups to generate an overall group associated with the advertisement request.

Abstract: A social networking system selects advertisements for a user based on user characteristics of the user in response to a request to present an advertisement to the user. To increase the number of advertisements eligible for presentation to the user, the social networking system associates the user with one or more cluster groups associated with targeting criteria that are not satisfied by the user's characteristics. To determine whether to associate a user with a cluster group, the social networking system determines a cluster score for the cluster group based on the user's characteristics. If the cluster score equals or exceeds a cluster cutoff score for the cluster group, the user is associated with the cluster group. The cluster cutoff score may be determined based on an estimated distribution of users so that a target number or percentage of users have cluster scores less than the cluster cutoff score.