Abstract: Systems and methods for optimizing content performance using creative extensions are provided. A content generation system receives request for a content item for presentation on a client device. The request includes an indication of a serving context for the content item. The content generation system uses a creative extension performance model and the serving context for the content item to calculate a predicted performance metric for the content item for multiple different potential creative extensions. Each of the potential creative extensions defines a different action that occurs in response to a user interaction with the content item. The content generation system selects one of the potential creative extensions based on the predicted performance metrics and generates a content item having the selected creative extension using data assets extracted from various data sources. The creative extension performance model is updated using event data from the client device.

Abstract: Systems and methods for optimizing content performance using creative extensions are provided. A content generation system receives request for a content item for presentation on a client device. The request includes an indication of a serving context for the content item. The content generation system uses a creative extension performance model and the serving context for the content item to calculate a predicted performance metric for the content item for multiple different potential creative extensions. Each of the potential creative extensions defines a different action that occurs in response to a user interaction with the content item. The content generation system selects one of the potential creative extensions based on the predicted performance metrics and generates a content item having the selected creative extension using data assets extracted from various data sources. The creative extension performance model is updated using event data from the client device.

Abstract: A computer-implemented method for generating creative type suggestions for an online content provider is provided. The method uses a computing device including a processor and a memory. The method includes training a first model with historical information including one or more of (i) serving performance of online advertisements and (ii) advertiser information. The method also includes computing a preliminary creative type suggestion using at least the first model. The method further includes modifying the preliminary creative type suggestion based at least in part on past suggestion performance to generate a final creative type suggestion. The method also includes presenting the final creative type suggestion to the online content provider.

Abstract: Systems and methods for optimizing content performance using creative extensions are provided. A content generation system receives request for a content item for presentation on a client device. The request includes an indication of a serving context for the content item. The content generation system uses a creative extension performance model and the serving context for the content item to calculate a predicted performance metric for the content item for multiple different potential creative extensions. Each of the potential creative extensions defines a different action that occurs in response to a user interaction with the content item. The content generation system selects one of the potential creative extensions based on the predicted performance metrics and generates a content item having the selected creative extension using data assets extracted from various data sources. The creative extension performance model is updated using event data from the client device.

Abstract: Systems and methods for optimizing content performance using creative extensions are provided. A content generation system receives request for a content item for presentation on a client device. The request includes an indication of a serving context for the content item. The content generation system uses a creative extension performance model and the serving context for the content item to calculate a predicted performance metric for the content item for multiple different potential creative extensions. Each of the potential creative extensions defines a different action that occurs in response to a user interaction with the content item. The content generation system selects one of the potential creative extensions based on the predicted performance metrics and generates a content item having the selected creative extension using data assets extracted from various data sources. The creative extension performance model is updated using event data from the client device.

Abstract: A computer-implemented method for generating creative type suggestions for an online content provider is provided. The method uses a computing device including a processor and a memory. The method includes training a first model with historical information including one or more of (i) serving performance of online advertisements and (ii) advertiser information. The method also includes computing a preliminary creative type suggestion using at least the first model. The method further includes modifying the preliminary creative type suggestion based at least in part on past suggestion performance to generate a final creative type suggestion. The method also includes presenting the final creative type suggestion to the online content provider.

Abstract: Systems and methods for optimizing content performance using creative extensions are provided. A content generation system receives request for a content item for presentation on a client device. The request includes an indication of a serving context for the content item. The content generation system uses a creative extension performance model and the serving context for the content item to calculate a predicted performance metric for the content item for multiple different potential creative extensions. Each of the potential creative extensions defines a different action that occurs in response to a user interaction with the content item. The content generation system selects one of the potential creative extensions based on the predicted performance metrics and generates a content item having the selected creative extension using data assets extracted from various data sources. The creative extension performance model is updated using event data from the client device.

Abstract: A computerized method for automatically generating display content includes receiving a uniform resource locator, wherein the uniform resource locator specifies a landing resource and extracting visual information from the landing resource, wherein the visual information defines one or more images, texts, and colors displayed on the landing resource. The method further includes selecting one or more images, one or more text snippets, and one or more colors based on the visual information extracted from the landing resource, generating a layout for a content item based on one or more of the selected images or selected text snippets, and assembling the content item by applying the selected images, the selected text snippets, and the selected colors to the generated layout.

Abstract: A computerized method for automatically generating display content includes receiving a uniform resource locator, wherein the uniform resource locator specifies a landing resource and extracting visual information from the landing resource, wherein the visual information defines one or more images, texts, and colors displayed on the landing resource. The method further includes selecting one or more images, one or more text snippets, and one or more colors based on the visual information extracted from the landing resource, generating a layout for a content item based on one or more of the selected images or selected text snippets, and assembling the content item by applying the selected images, the selected text snippets, and the selected colors to the generated layout.

Abstract: A computerized method for automatically generating display content includes receiving a uniform resource locator, wherein the uniform resource locator specifies a landing resource and extracting visual information from the landing resource, wherein the visual information defines one or more images, texts, and colors displayed on the landing resource. The method further includes selecting one or more images, one or more text snippets, and one or more colors based on the visual information extracted from the landing resource, generating a layout for a content item based on one or more of the selected images or selected text snippets, and assembling the content item by applying the selected images, the selected text snippets, and the selected colors to the generated layout.

Abstract: Systems and methods for optimizing content performance using click types are provided. A content generation system receives request for a content item for presentation on a client device. The request includes an indication of a serving context for the content item. The content generation system uses a click type performance model and the serving context for the content item to calculate a predicted performance metric for the content item for multiple different potential click types. Each of the potential click types defines different criteria for triggering an event that occurs in response to a user interaction with the content item when the defined criteria are satisfied. The content generation system selects one of the potential click types based on the predicted performance metrics and generates a content item having the selected click type. The click type performance model is updated using event data from the client device.

Abstract: A computer-implemented method for generating creative type suggestions for an online content provider is provided. The method uses a computing device including a processor and a memory. The method includes training a first model with historical information including one or more of (i) serving performance of online advertisements and (ii) advertiser information. The method also includes computing a preliminary creative type suggestion using at least the first model. The method further includes modifying the preliminary creative type suggestion based at least in part on past suggestion performance to generate a final creative type suggestion. The method also includes presenting the final creative type suggestion to the online content provider.

Abstract: Systems and methods for optimizing content performance using click types are provided. A content generation system receives request for a content item for presentation on a client device. The request includes an indication of a serving context for the content item. The content generation system uses a click type performance model and the serving context for the content item to calculate a predicted performance metric for the content item for multiple different potential click types. Each of the potential click types defines different criteria for triggering an event that occurs in response to a user interaction with the content item when the defined criteria are satisfied. The content generation system selects one of the potential click types based on the predicted performance metrics and generates a content item having the selected click type. The click type performance model is updated using event data from the client device.

Abstract: Systems and methods for optimizing content performance using creative extensions are provided. A content generation system receives request for a content item for presentation on a client device. The request includes an indication of a serving context for the content item. The content generation system uses a creative extension performance model and the serving context for the content item to calculate a predicted performance metric for the content item for multiple different potential creative extensions. Each of the potential creative extensions defines a different action that occurs in response to a user interaction with the content item. The content generation system selects one of the potential creative extensions based on the predicted performance metrics and generates a content item having the selected creative extension using data assets extracted from various data sources. The creative extension performance model is updated using event data from the client device.

Abstract: A computerized method for automatically generating display content includes receiving a uniform resource locator, wherein the uniform resource locator specifies a landing resource and extracting visual information from the landing resource, wherein the visual information defines one or more images, texts, and colors displayed on the landing resource. The method further includes selecting one or more images, one or more text snippets, and one or more colors based on the visual information extracted from the landing resource, generating a layout for a content item based on one or more of the selected images or selected text snippets, and assembling the content item by applying the selected images, the selected text snippets, and the selected colors to the generated layout.

Abstract: Methods, systems, and apparatuses, including computer programs encoded on computer readable media, for selecting images based on textual description are provided. Candidate images may be stored, for example, in an image repository. A textual description for an advertisement is received. Characteristic data, such as classification results and a feature vector, are generated based on the textual description for the advertisement. A first correlation score for each candidate image based on the characteristic data, for example, the classification results of the textual description, can be calculated. A second correlation score for each candidate image based on the characteristic data, for example, the feature vector of the textual description, can be calculated. An image can be selected from the candidate images based on the first correlation score and the second correlation score of the selected image.