Abstract: An online system generates dynamically optimized sponsored content for a target user of the online system. Each sponsored content item comprises optimal creatives selected for a target user from a set of creatives provided by a content provider. Each type of creative (e.g. title, image, video, descriptive text), has a trained creative model to generate a prediction score for a creative of the same type based on the features of the creative and the characteristics of the target user. The prediction score of a creative indicates the likelihood that the target user will interact with a sponsored content item that includes that particular creative. The online system selects a creative for each type and assembles the selected creatives into a sponsored content item for the target user. For a different user, a different sponsored content item having different creatives chosen from the same set of creatives is generated.

Abstract: An online system provides feedback to a content provider creating a content item for a target audience. The feedback may include a score, recommendation, or error notification for a creative such as an image, video, or text to be included in the content item. The score indicates a likelihood that users of the online system will interact with the content item having the creative. Modifying the content item based on recommendations may result in a different score for the content item. The online system trains a machine learning model to generate the scores. The model learns which creatives are popular among particular audiences. The online system provides error notifications if the content item violates a rule. The online system can generate the content item even if there are rule violations. The feedback is displayed inline on a graphical user interface while the content provider is creating the content item.

Abstract: An online system generates dynamically optimized sponsored content for a target user of the online system. Each sponsored content item comprises optimal creatives selected for a target user from a set of creatives provided by a content provider. Each type of creative (e.g. title, image, video, descriptive text), has a trained creative model to generate a prediction score for a creative of the same type based on the features of the creative and the characteristics of the target user. The prediction score of a creative indicates the likelihood that the target user will interact with a sponsored content item that includes that particular creative. The online system selects a creative for each type and assembles the selected creatives into a sponsored content item for the target user. For a different user, a different sponsored content item having different creatives chosen from the same set of creatives is generated.