Abstract: An online system generates a cluster group and uses membership in the cluster group as an eligibility criteria for presenting a content item. The online system receives a request from a third party system to present the content item and an identification of a target action associated with the content item. The online system also receives information about users who performed a target action and users who performed related actions other than the target action on one or more webpages associated with the third party system. The online system forms a multitask neural network and uses the multitask neural network to train a cluster model based on the received information. The online system applies the cluster model to candidate users who have not performed the target action and determines whether to include a candidate user into the cluster group based on output of the cluster model.

Abstract: An online system generates a cluster group and uses membership in the cluster group as an eligibility criteria for presenting a content item. The online system receives a request from a third party system to present the content item. The online system also receives identification information about users who have visited webpages associated with the third party system and descriptive information associated with the webpages. Based on the descriptive information, the online system extracts tags for the webpages and classifies the webpages into one or more categories that include a category associated with the content item. The online system generates a seed group that includes users who visited webpages in the category associated with the content item. The online system further expands the seed group to a cluster group by applying a cluster model to one or more characteristics of each candidate user not included in the seed group.

Abstract: An online system defines a new group of users of the online system for a content provider to use as an audience for its content. The online system generates the audience by first allowing the content provider to specify a subset of the users of the online system, along with a weight for each specified user. The online system then performs a lookalike expansion on the specified subset to obtain a larger group of users of the online system who are similar to the specified subset. This lookalike expansion is weighted using the weights provided for each of the members of the specified subset. Once the resulting expanded audience is generated, the online system performs a content selection process for content to present to users of the online system, where membership in the cluster group is used as an eligibility criteria in the content selection process.

Abstract: An online system receives an advertisement request (“ad request”) including an advertisement, targeting criteria identifying characteristics of users eligible to be presented with the advertisement, and one more rules associating weights with characteristics of users. Based on the rules included in the ad request, the online system generates a cluster model that is applied to characteristics of users who do not have characteristics satisfying the targeting criteria in the ad request to generate cluster scores. Users with cluster scores equaling or exceeding a cluster group cutoff score are identified as eligible to be presented with the advertisement in the ad request despite not having characteristics satisfying the targeting criteria in the ad request. Hence, the ad request is eligible for presentation to users having characteristics satisfying the ad request's targeting criteria or having cluster scores equaling or exceeding the cluster group cutoff score.