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 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: Operating a range slider. A method includes displaying a range slider in a graphical user interface. The method further includes detecting a minimum selecting user interaction on a graphical user interface. The method further includes placing the minimum slider thumb at value on the slider track by placing the inside edge of the minimum slider thumb on the value to select the minimum endpoint of the selected range at the inside edge of the minimum slider thumb. The method further includes detecting a maximum selecting user interaction on the graphical user interface. The method further includes placing the maximum slider thumb at a value on the slider track by placing the inside edge of the maximum slider thumb on the value to select the maximum endpoint of the selected range at the inside edge of the maximum slider thumb.

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 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 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 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.

Abstract: Concepts and technologies are described herein for providing a range filter user interface with selectable range controls that toggle between inclusive and exclusive modes. The range controls visually indicate configuration in an inclusive mode or an exclusive mode. Upon selection of a range control, the range control switches between inclusive and exclusive modes, and provides visual indication of the current mode.

Abstract: Concepts and technologies are described herein for providing a range filter user interface with selectable range controls that toggle between inclusive and exclusive modes. The range controls visually indicate configuration in an inclusive mode or an exclusive mode. Upon selection of a range control, the range control switches between inclusive and exclusive modes, and provides visual indication of the current mode.

Abstract: Operating a range slider. A method includes displaying a range slider in a graphical user interface. The method further includes detecting a minimum selecting user interaction on a graphical user interface. The method further includes placing the minimum slider thumb at value on the slider track by placing the inside edge of the minimum slider thumb on the value to select the minimum endpoint of the selected range at the inside edge of the minimum slider thumb. The method further includes detecting a maximum selecting user interaction on the graphical user interface. The method further includes placing the maximum slider thumb at a value on the slider track by placing the inside edge of the maximum slider thumb on the value to select the maximum endpoint of the selected range at the inside edge of the maximum slider thumb.

Abstract: Program source code is annotated to support dataflow analysis or other program analysis, without requiring changes to compilers. Annotation statements are embedded inside comments or other non-code-generative portions of the source code. The annotations can be used to express contracts at routine boundaries, allowing an analyzer to check the global correctness of the source code through modular (local) analysis, with performance that is linear in the number of routines. In particular, annotated SQL source code may be analyzed to identify SQL injection vulnerabilities.

Abstract: Program source code is annotated to support dataflow analysis or other program analysis, without requiring changes to compilers. Annotation statements are embedded inside comments or other non-code-generative portions of the source code. The annotations can be used to express contracts at routine boundaries, allowing an analyzer to check the global correctness of the source code through modular (local) analysis, with performance that is linear in the number of routines. In particular, annotated SQL source code may be analyzed to identify SQL injection vulnerabilities.