Abstract: An online system generates dynamically optimized content items composed of creatives selected from a set of creatives provided by a content provider according to a set of rules associated with the creatives. Creatives include the title, image, video, descriptive text and other different types of components. The online system also receives rules describing one or more actions that can be performed on each of the creatives and under what condition for the assembly of the content item. For a target user of the content item, the online system applies the rules to remove creatives that violate the rules. Each creative that satisfies the rules is analyzed and ranked based on the likelihood that the target user will interact with a content item that includes that particular creative. 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 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 receives a set of creatives provided by a content provider, and presents one or more pseudo-assembled content items composed of the different combinations of the received creatives on a user interface to the content provider. A pseudo-assembled content item includes one or more creatives to be included in a final content item that are placed in their positions in the display interface, but the content item has not yet undergone assembly or creation. The positions of the creatives are defined by one or more placement rules provided by the content provider. The content provider can interact with the user interface to swap different creatives into the content item. The content provider can visually preview different content item candidates assembled from different permutations of creatives from the set of creatives of the content item before creating the final content item.

Abstract: An online system displays a content item generated from the ranked candidate creatives after an ad campaign or during the running of an ad campaign of the content item. The online system may present the content item through a display interface and allows the content provider to view the content item that is composed of different combinations of candidate creatives in view of performance statistics of the creatives, e.g. click through rate, number of “Likes”, number of audiences reached by the ad campaign of the content item, and viewers' engagement with the individual creatives or the content item as a whole. The online system can select a number of top ranked creatives for a content item for a target audience and adjust the selected creatives' ranking scores based on the performance statistics associated with the selected creatives.

Abstract: An online system generates dynamically optimized content items composed of creatives selected from a set of creatives provided by a content provider according to a set of rules associated with the creatives. Creatives include the title, image, video, descriptive text and other different types of components. The online system also receives rules describing one or more actions that can be performed on each of the creatives and under what condition for the assembly of the content item. For a target user of the content item, the online system applies the rules to remove creatives that violate the rules. Each creative that satisfies the rules is analyzed and ranked based on the likelihood that the target user will interact with a content item that includes that particular creative. 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 receives a set of creatives provided by a content provider, and presents one or more pseudo-assembled content items composed of the different combinations of the received creatives on a user interface to the content provider. A pseudo-assembled content item includes one or more creatives to be included in a final content item that are placed in their positions in the display interface, but the content item has not yet undergone assembly or creation. The positions of the creatives are defined by one or more placement rules provided by the content provider. The content provider can interact with the user interface to swap different creatives into the content item. The content provider can visually preview different content item candidates assembled from different permutations of creatives from the set of creatives of the content item before creating the final content item.

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 provides a content data model to content providers for optimizing content creation. The content data model is a hierarchical model with multiple levels for content creation, e.g., campaign level, content item set level and content item level. At each level of the content data model, a content provider can specify certain information concerning the content creation at that level, such as optimization goal for each level. The information specified at each level of the content data model is applied to all elements under that level by the online system during the content creation process. With the content data model, a content provider can efficiently design creative campaigns by specifying objectives, optimization goals, target audiences and budgets, etc., at different design levels. The online system dynamically optimizes content item creation based on information about creatives to be included in a content item for a target user.

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.