A promotion offering system and method is disclosed. The promotion offering system and method selects consumers for a test promotion that has one or more attributes, and is configured to generate test data for multiple consumer groupings. The promotion offering system and method may use the test data in analyzing whether to send a promotion to a consumer. For example, the promotion offering system and method may use one algorithm to generate a list of ranked promotions, and may use the test data in order to adjust the list of ranked promotions (such as replacing a highest ranked promotion with another promotion).

This application relates to apparatus and methods for providing a unified serving platform that allows for the reusability of machine learning models across a plurality of websites to determine personalized content. For example, a computing device trains a machine learning model with session data identifying browsing events and transaction data identifying purchasing events for a plurality of users. The computing device receives and stores session data and transaction data associated with a first website for the customer. The computing device may then receive a request for content to display to the customer on a second website. The computing device generates label data based on the session data and transaction data associated with the first website, and executes the trained machine learning model with the label data. Based on execution of the trained machine learning model, the computing device generates content to display on the second website, and transmits the content.

Provided is a system for creating a news article containing an indirect advertisement, the system including an advertisement database including an advertisement item and an indirect advertisement composed of text matching the advertisement item, an advertisement search unit configured to, when a text-type original news article to be exposed to a webpage is input, search the database for an indirect advertisement candidate matching the original news article and select an advertisement candidate list, an advertisement position determination unit configured to determine a paragraph of the original news article into which a selected advertisement is to be inserted, and an advertisement phrase creation unit configured to create a news article containing an advertisement by inserting the selected advertisement into the paragraph of the original news article and expose the created news article.

A processor receives requests for content items and identifies a first subset of machine learning (ML) models that satisfy a reliability criterion and a second subset of ML models that fail to satisfy the reliability criterion, wherein each ML model is associated with a respective content template and is trained to output a probability that a target associated with an input set of characteristics would perform a target action responsive to being presented with a content item generated based on the respective associated content template. For each request in a first group, the processor inputs the respective set of characteristics associated with the request into each ML model of the first subset, selects a content template, and generates a content item based on the selected content template. For each request in the second group, the processor generates a content item based on a content template associated with the second subset.

Provided are systems and methods for face reenactment. An example method includes receiving visual data including a visible portion of a source face, determining, based on the visible portion of the source face, a first portion of source face parameters associated with a parametric face model, where the first portion corresponds to the visible portion, predicting, based partially on the visible portion of the source face, a second portion of the source face parameters, where the second portion corresponds to the rest of the source face, receiving a target video that includes a target face, determining, based on the target video, target face parameters associated with the parametric face model and corresponding to the target face, and synthesizing, using the parametric face model, based on the source face parameters and the target face parameters, an output face that includes the source face imitating a facial expression of the target face.

Systems and methods for redeeming a reward held by an individual are described. A method for redeeming a reward includes determining threshold criteria for provision of a targeted reward redemption offer, identifying at least one individual based upon the threshold criteria, determining the targeted reward redemption offer, and providing the targeted reward redemption offer to the identified individual. A response to the provided targeted reward redemption offer may be received, and an account of the identified individual may be adjusted in accordance with the targeted reward redemption offer and the received response.

Various implementations of the invention for predicting customer behavior are described. Various implementations of the invention comprise an embedding component configured to receive and embed sequential inputs regarding a plurality of customer interactions with an online presence of a client; a plurality of causal dilated convolutional CDC elements configured to receive the embedded sequential inputs and to output a feature vector, where each CDC element comprises two causal dilated convolutions with regularization that is bypassed with a skip connection; a plurality of dense neural network elements configured to receive the feature vector and non-sequential inputs regarding a plurality of other customer interactions with the client, where each of the plurality of dense neural network elements comprises two dense neural networks with regularization that is bypassed with a skip connection; and an output generator configured to receive the output from the plurality of dense neural network elements and to generate a distribution of times over which a particular customer event will occur and/or a likelihood estimation that the particular customer event will occur within a particular time period.

A system and method identify and provide content using identification entries. Product identification information for a product listing is received, and a matching identification entry is selected from a set of identification entries using the product identification information. The matching identification entry is assigned to the product listing for use in selecting content regarding the product listing for presentation.

A computer receives a request for graphical display source code for a computerized graphical advertisement display, and retrieves seed images including a plurality of seed image features. The computer generates candidate images based on the one or more seed images, where the computer alters a first aspect of a seed image to generate an altered seed image having a plurality of altered seed image features and the computer alters a second aspect of the altered seed image to generate a candidate image having a plurality of candidate image features. The computer generates candidate image scores based upon a context of the advertisement display and the plurality of candidate image features. The computer selects an image from the candidate images based on the candidate image scores and generates the graphical display source code based on the selected image, a size of the advertisement display, and display capabilities of the user device.

Example methods, apparatus, systems and articles of manufacture (e.g., physical storage media) to perform probabilistic modeling for anonymized data integration and measurement of sparse and weakly-labeled datasets are disclosed. An apparatus includes a training controller to train a neural network to produce a trained neural network to output model parameters of a probability model, a model evaluator to execute the trained neural network on input data specifying a time of day, a media source, and at least one feature different from the time of day and the media source to determine one or more first model parameters of the probability model, and a ratings metric generator to evaluate the probability model based on input census data to determine a ratings metric corresponding to the time of day, the media source, and the at least one feature, the probability model configured with the one or more first model parameters.