Abstract: An online system receives multiple candidate components for including in content items to be presented to online system users. Upon identifying an opportunity to present content to a subject user of the online system, the online system dynamically generates an optimal content item for presentation to the subject user that includes one or more candidate components. Candidate components included in the optimal content item are associated with a predicted marginal effect on a performance metric associated the optimal content item. This marginal effect may be predicted using a machine-learned model that is trained using historical performance information about content items that were presented to viewing users of the online system having at least a threshold measure of similarity to the subject user and one or more features associated with candidate components included in these content items and in the optimal content item.

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 multiple candidate components for including in content items to be presented to online system users. Upon identifying an opportunity to present content to a subject user of the online system, the online system dynamically generates an optimal content item for presentation to the subject user that includes one or more candidate components. Candidate components included in the optimal content item are selected by predicting an affinity of the subject user for each candidate component. The affinity of the subject user for a candidate component may be predicted using a machine-learned model that is trained using historical performance information about content items including the candidate component that were presented to viewing users of the online system having at least a threshold measure of similarity to the subject user. Components of content items used to train the model may be selected using a heuristic (e.g., Thompson sampling).

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 generates dynamically optimized content for a target user of the online system. To do so, the online system receives content component from a content provider system and generates a pool of content items assembled from the content components for a target audience. The online system presents the content items in the pool to users of the online system and tracks the performance of each content item. The online system modifies the pool of content items to eliminate content components that are poorly performing while propagating content components that are highly performing. Therefore, over multiple iterations, the final pool of content items is increasingly tailored for a target audience. Upon receiving a request to present a content item for a user that meets the characteristic of the target audience, the online system selects a content item from the final pool to be presented to the user.

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 receives multiple candidate components for including in content items to be presented to online system users. Upon identifying an opportunity to present content to a subject user of the online system, the online system dynamically generates an optimal content item for presentation to the subject user that includes one or more candidate components. Candidate components included in the optimal content item are selected by predicting an affinity of the subject user for each candidate component. The affinity of the subject user for a candidate component may be predicted using a machine-learned model that is trained using historical performance information about content items including the candidate component that were presented to viewing users of the online system having at least a threshold measure of similarity to the subject user. Components of content items used to train the model may be selected using a heuristic (e.g., Thompson sampling).

Abstract: An online system receives multiple candidate components for including in content items to be presented to online system users. Upon identifying an opportunity to present content to a subject user of the online system, the online system dynamically generates an optimal content item for presentation to the subject user that includes one or more candidate components. Candidate components included in the optimal content item are associated with a predicted marginal effect on a performance metric associated the optimal content item. This marginal effect may be predicted using a machine-learned model that is trained using historical performance information about content items that were presented to viewing users of the online system having at least a threshold measure of similarity to the subject user and one or more features associated with candidate components included in these content items and in the optimal 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.