Abstract: A method includes storing at least one image performance score for each of a set of images, the set of images comprising a plurality of subsets of images, each subset corresponding with a different web page of a plurality of web pages, the at least one image performance score for an image indicating a likelihood that a user will interact with the image; determining a web page score for each of the plurality of web pages based on one or more image performance scores of the subset of images that corresponds with the web page; receiving a query comprising one or more keywords or images; selecting a set of web pages by applying a search engine machine learning model to the one or more keywords and the web page score for each of the plurality of web pages; and presenting the set of web pages at a computing device.

Abstract: A method includes displaying a first user interface including a target image and a task selection element, in response to a selection of the task selection element, displaying a second user interface including a target audience selection element, in response to a selection of a target audience using the target audience selection element, displaying a third user interface including a benchmark selection element, in response to a selection of a benchmark using the benchmark selection element, executing a target audience machine learning model using as input the target image to generate a performance score, calculating a benchmark score for the target image based on the performance score of the target image and performance scores for the images of the benchmark, and displaying a fourth user interface including the target image and the benchmark score.

Abstract: The present disclosure describes a method comprising receiving a plurality of training images; executing a feature extraction machine learning model to generate a plurality of training embeddings for a plurality of training images each in an embedding space; training a content scoring machine learning model using the plurality of training embeddings to generate performance scores for content items based on embeddings in the embedding space; receiving a set of text; executing the feature extraction machine learning model using the set of text to generate a text embedding in the same embedding space as the training embeddings for the plurality of training images; generating, using the content scoring machine learning model, a text performance score for the set of text using the text embedding in the embedding space; and generating a record identifying the text performance score for the set of text.

Abstract: A method for training a text generation machine learning model for prompt generation includes receiving a plurality of training text strings; executing a text generation machine learning model using each of the plurality of training text strings to generate a plurality of training generated text strings, each of the plurality of generated text strings corresponding to generating a content item; executing a content scoring machine learning model using each of the plurality of generated text strings to generate a text performance score for each of the plurality of training generated text strings; for each of the plurality of generated text strings, adjusting one or more weights or parameters of the text generation machine learning model proportional to the text performance score for the generated text string.

Abstract: A method includes receiving a pointer to a plurality of images of a web page, the web page configured to display the plurality of images in a first arrangement; retrieving, using the pointer, the plurality of images of the web page; executing at least one machine learning model using the plurality of images from the web page as input to generate at least one image performance score for each image of the plurality of images, the at least one machine learning model trained based on a training set of images labeled based at least on interaction data corresponding to images of the training set of images; and rearranging the plurality of images on the web page to a second arrangement according to the at least one image performance score generated for each image of the plurality of images of the web page.

Abstract: A method may include executing a neural network to extract a first plurality of features from a plurality of first training images and a second plurality of features from a second training image; generating a model comprising a first image performance score for each of the plurality of first training images and a feature weight for each feature, the feature weight for each feature of the first plurality of features calculated based on an impact of a variation in the feature on first image performance scores of the plurality of first training images; training the model by adjusting the impact of a variation of each of a first set of features that correspond to the second plurality of features; executing the model using a third set of features from a candidate image to generate a candidate image performance score; and generating a record identifying the candidate image performance score.

Abstract: A method includes storing at least one image performance score for each of a set of images, the set of images comprising a plurality of subsets of images, each subset corresponding with a different web page of a plurality of web pages, the at least one image performance score for an image indicating a likelihood that a user will interact with the image; determining a web page score for each of the plurality of web pages based on one or more image performance scores of the subset of images that corresponds with the web page; receiving a query comprising one or more keywords or images; selecting a set of web pages by applying a search engine machine learning model to the one or more keywords and the web page score for each of the plurality of web pages; and presenting the set of web pages at a computing device.

Abstract: A method includes storing a database comprising a plurality of pointers to web pages and identifiers of entities associated with the plurality of pointers; receiving a first request comprising a first identifier; identifying subset of the plurality of pointers from the database responsive to each pointer of the subset having a stored association with a first identification that matches the first identifier; responsive to identifying the subset of the plurality of pointers, establishing, via one or more pointers, a connection with a server hosting a set of web pages associated with the subset of the plurality of pointers; retrieving one or more images or videos from each of the set of web pages over the established connection; calculating a performance score for each of the one or more images or videos; and generating a record identifying the performance score for each of the one or more images or videos.

Abstract: A method includes displaying a first user interface including a target image and a task selection element, in response to a selection of the task selection element, displaying a second user interface including a target audience selection element, in response to a selection of a target audience using the target audience selection element, displaying a third user interface including a benchmark selection element, in response to a selection of a benchmark using the benchmark selection element, executing a target audience machine learning model using as input the target image to generate a performance score, calculating a benchmark score for the target image based on the performance score of the target image and performance scores for the images of the benchmark, and displaying a fourth user interface including the target image and the benchmark score.

Abstract: A method includes accessing a web-based property over a network; storing a plurality of images or videos from the web-based property and associations between the plurality of images or videos and a target audience identifier responsive to the web-based property having a stored association with the target audience identifier; retrieving the plurality of images or videos from the database responsive to each of the plurality of images or videos having stored associations with the target audience identifier; executing a neural network to generate a performance score for each of the plurality of images or videos; calculating a target audience benchmark; executing the neural network to generate a first performance score for a first image or video and a second performance score for a second image or video; comparing the first performance score and the second performance score to the benchmark; and generating a record identifying the first image or video.

Abstract: A method includes receiving a pointer to a plurality of images of a web page, the web page configured to display the plurality of images in a first arrangement; retrieving, using the pointer, the plurality of images of the web page; executing at least one machine learning model using the plurality of images from the web page as input to generate at least one image performance score for each image of the plurality of images, the at least one machine learning model trained based on a training set of images labeled based at least on interaction data corresponding to images of the training set of images; and rearranging the plurality of images on the web page to a second arrangement according to the at least one image performance score generated for each image of the plurality of images of the web page.

Abstract: A method includes accessing a web-based property over a network; storing a plurality of images or videos from the web-based property and associations between the plurality of images or videos and a target audience identifier responsive to the web-based property having a stored association with the target audience identifier; retrieving the plurality of images or videos from the database responsive to each of the plurality of images or videos having stored associations with the target audience identifier; executing a neural network to generate a performance score for each of the plurality of images or videos; calculating a target audience benchmark; executing the neural network to generate a first performance score for a first image or video and a second performance score for a second image or video; comparing the first performance score and the second performance score to the benchmark; and generating a record identifying the first image or video.

Abstract: A method includes receiving a first image; extracting a first set of features from the first image; executing a first machine learning model using the extracted first set of features as input to generate a first image performance score for the first image; iteratively executing, using the first set of features as input for each execution, a generative machine learning model to generate a plurality of generated images; extracting a second set of features from each of the plurality of generated images; iteratively executing the first machine learning model using a different second set of features as input for each execution to generate a generated image performance score for each of the plurality of generated images; and transmitting a defined number of the plurality of generated images to a computing device, the defined number of generated images determined based on the generated image performance scores.

Abstract: A method includes storing at least one image performance score for each of a set of images, the set of images comprising a plurality of subsets of images, each subset corresponding with a different web page of a plurality of web pages, the at least one image performance score for an image indicating a likelihood that a user will interact with the image; determining a web page score for each of the plurality of web pages based on one or more image performance scores of the subset of images that corresponds with the web page; receiving a query comprising one or more keywords or images; selecting a set of web pages by applying a search engine machine learning model to the one or more keywords and the web page score for each of the plurality of web pages; and presenting the set of web pages at a computing device.

Abstract: A method includes receiving a first image; extracting a first set of features from the first image; executing a first machine learning model using the extracted first set of features as input to generate a first image performance score for the first image; iteratively executing, using the first set of features as input for each execution, a generative machine learning model to generate a plurality of generated images; extracting a second set of features from each of the plurality of generated images; iteratively executing the first machine learning model using a different second set of features as input for each execution to generate a generated image performance score for each of the plurality of generated images; and transmitting a defined number of the plurality of generated images to a computing device, the defined number of generated images determined based on the generated image performance scores.

Abstract: A method may include executing a neural network to extract a first plurality of features from a plurality of first training images and a second plurality of features from a second training image; generating a model comprising a first image performance score for each of the plurality of first training images and a feature weight for each feature, the feature weight for each feature of the first plurality of features calculated based on an impact of a variation in the feature on first image performance scores of the plurality of first training images; training the model by adjusting the impact of a variation of each of a first set of features that correspond to the second plurality of features; executing the model using a third set of features from a candidate image to generate a candidate image performance score; and generating a record identifying the candidate image performance score.

Abstract: A method includes displaying a first user interface including a target image and a task selection element, in response to a selection of the task selection element, displaying a second user interface including a target audience selection element, in response to a selection of a target audience using the target audience selection element, displaying a third user interface including a benchmark selection element, in response to a selection of a benchmark using the benchmark selection element, executing a target audience machine learning model using as input the target image to generate a performance score, calculating a benchmark score for the target image based on the performance score of the target image and performance scores for the images of the benchmark, and displaying a fourth user interface including the target image and the benchmark score.

Abstract: A method includes displaying a first user interface including a target image and a task selection element, in response to a selection of the task selection element, displaying a second user interface including a target audience selection element, in response to a selection of a target audience using the target audience selection element, displaying a third user interface including a benchmark selection element, in response to a selection of a benchmark using the benchmark selection element, executing a target audience machine learning model using as input the target image to generate a performance score, calculating a benchmark score for the target image based on the performance score of the target image and performance scores for the images of the benchmark, and displaying a fourth user interface including the target image and the benchmark score.

Abstract: A method includes storing a database comprising a plurality of pointers to web pages and identifiers of entities associated with the plurality of pointers; receiving a first request comprising a first identifier; identifying subset of the plurality of pointers from the database responsive to each pointer of the subset having a stored association with a first identification that matches the first identifier; responsive to identifying the subset of the plurality of pointers, establishing, via one or more pointers, a connection with a server hosting a set of web pages associated with the subset of the plurality of pointers; retrieving one or more images or videos from each of the set of web pages over the established connection; calculating a performance score for each of the one or more images or videos; and generating a record identifying the performance score for each of the one or more images or videos.

Abstract: A method may include executing a neural network to extract a first plurality of features from a plurality of first training images and a second plurality of features from a second training image; generating a model comprising a first image performance score for each of the plurality of first training images and a feature weight for each feature, the feature weight for each feature of the first plurality of features calculated based on an impact of a variation in the feature on first image performance scores of the plurality of first training images; training the model by adjusting the impact of a variation of each of a first set of features that correspond to the second plurality of features; executing the model using a third set of features from a candidate image to generate a candidate image performance score; and generating a record identifying the candidate image performance score.