Abstract: An online system stores user profiles of users performing past user interactions with content items. The system receives a new content item and extracts a new feature vector from an image in the new content item using image analysis. The system generates, by a machine learning model, a demographic criteria vector based on the new feature vector. The machine learning model is configured based on the user profiles of the users performing the past user interactions with the plurality of content items to receive a feature vector for a content item and generate a demographic criteria vector based on the feature vector. The demographic criteria vector indicates a likelihood of a user with a user profile matching the demographic criteria vector interacting with the content item exceeding a threshold. The system sends the demographic criteria vector to a content provider for targeting the new content item.

Abstract: An online system receives related content items from a content provider for delivery over a period of time. The system determines a rate of delivery of each related content item to users of the online system. The system delivers the related content items based on the determined rates of delivery. The system monitors a rate of client interactions with the related content items. The system adjusts the rate of delivery of each related content item based on the rate of client interactions associated with the related content item, such that higher performing related content is delivered more than lower performing related content. If the rate of delivery of the related content item decreases below a threshold, the system sets the adjusted rate of delivery of the related content item to the threshold. The system delivers the related content items to client devices based on the adjusted rates of delivery.

Abstract: An online system receives related content items from a content provider for delivery over a period of time. The system determines a rate of delivery of each related content item to users of the online system. The system delivers the related content items based on the determined rates of delivery. The system monitors a rate of client interactions with the related content items. The system adjusts the rate of delivery of each related content item based on the rate of client interactions associated with the related content item, such that higher performing related content is delivered more than lower performing related content. If the rate of delivery of the related content item decreases below a threshold, the system sets the adjusted rate of delivery of the related content item to the threshold. The system delivers the related content items to client devices based on the adjusted rates of delivery.

Abstract: An online system stores user profiles of users performing past user interactions with content items. The system receives a new content item and extracts a new feature vector from an image in the new content item using image analysis. The system generates, by a machine learning model, a demographic criteria vector based on the new feature vector. The machine learning model is configured based on the user profiles of the users performing the past user interactions with the plurality of content items to receive a feature vector for a content item and generate a demographic criteria vector based on the feature vector. The demographic criteria vector indicates a likelihood of a user with a user profile matching the demographic criteria vector interacting with the content item exceeding a threshold. The system sends the demographic criteria vector to a content provider for targeting the new content item.