A video delivery service may display different content on an interface, such as ad campaign content, media program campaign content, and recommendation content. For example, the video delivery service may communicate with an ad engine to determine ad campaign content that is available and ad campaign scores to rate the ad campaign content. The video delivery service may also communicate with a media program campaign engine to determine any media program campaign content for the user and media program campaign scores to rate the media program campaign content. Then, the video delivery service may communicate with a recommendation engine to rate the relevance of the ad campaign content or media program campaign content to the user. After receiving the ad campaign content, media program campaign content, and recommendation content, the video delivery service determines which of the ad campaign content, media program campaign content, and recommendation content to display in the interface.

Computer-implemented method and system to display targeted ads based on ranking output of transactions. The computer-implemented method includes ranking likely sellers of real estate. Further, the computer-implemented method includes ranking likely refinances or loans on real estate. Furthermore, the computer-implemented method includes matching visitors of websites to properties in a property database that includes owner details and property details. Moreover, the computer-implemented method includes displaying most relevant ads to users based on rank of property owned.

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.

System/method for advertising content demonstration management preforming compilation of a set of rules for advertising content demonstration, and a corresponding set of advertising materials; saving of the set of advertising materials in a database; scanning of visitors around a broadcasting unit, by means of a visitor's data acquisition device; recognition and classifying of visitors according to data acquired, taking into account information from external data sources; selecting a rule from a set of the rules for advertising content demonstration, which coincides the most closely to the results of said classification, taking into account information from external data source; demonstration of advertising materials corresponding to a selected rule; registering of viewings of advertising materials by a visitor by means of at least one visitor's data acquisition device; saving information about viewings in the database; and compilation of a report about accomplishment of planned demonstration quantitative targets specified in an application.

Various embodiments of an apparatus, method(s), system(s) and a computer program product(s) described herein are directed to a Offer Engine. The Offer Engine extracts one or more features from data associated with a first user requesting access to a portion of content of a content corpus. The Offer Engine feeds at least one of the features of the first user into a decision tree. The decision tree has multiple levels, wherein at least one level comprises a plurality of leaves and each respective leaf implements at least one machine learning model. The Offer Engine determines whether to provide the first user with a subscription fee offer first option or a non-subscription fee offer second option based at least in part on output of the decision tree.

A system and methods for simulating human usage of mobile devices by simulating human behavior patterns operating mobile devices and using the simulation of human usage to obtain advertising or other online content specific to certain entities that is displayed on the mobile devices. The advertising or other online content specific to the certain entities is transmitted to an analytics server, where the advertising or other online content is interpreted and deciphered to obtain particular elements that relate to the advertising and online content including, but not limited to, an identification of the specific entity, the size of the advertising or other online content, the locations where the advertising or other online content appears, and the path by which the advertising or other online content travels to the mobile devices for display.

A system for analyzing user consumption of content on web pages or determining user engagement with content comprising a data collection script and an content-engagement-inference engine. The data collection script determines one or more content elements to be measured from a web page and collects description data associated with the one or more content elements. The description data describes information about the one or more content elements. The content-engagement-inference engine creates an inference or analytics result based on the description data. The inference or analytics result describes how the one or more content elements have been consumed by users and then provides the inference or analytics result for users to designated entities.

Systems and methods are presented for managing electronic promotion correspondence sent to consumers. A system may manage electronic promotion correspondence sent on a per-consumer basis. The system may access multiple electronic promotion correspondences generated for a particular consumer, select an electronic promotion correspondence from among the multiple electronic promotion correspondences, and determine to send the electronic promotion correspondence to the consumer according to any number of factors. The system may determine a target time to send the first electronic promotion correspondence to the consumer and selected communication channel to send the electronic promotion correspondence through.

There are provided systems and methods for iteratively improving an advertisement response model. A payment provider may perform operations that include training an advertisement response model using a training data set. The operations include determining that a first accuracy value corresponding to the advertisement response model is less than a accuracy value threshold. The operations further include identifying, based on executing the advertisement response model using a target data set that is different from the training data set, one or more units from the target data set for which to run the advertising campaign. The operations also include receiving one or more responses corresponding to a run of the advertising campaign with respect to the identified one or more units from the target data set and updating the training data set based on the one or more responses. The operations further include training an advertisement response model using resulting training data and repeating the operations as long as the accuracy value of the resulting model stays below the threshold or until the increase in the accuracy value with each iteration becomes unprofitable with respect to the costs of acquiring responses from further units from the target dataset.

An approach is defined to establish consumption analytics of network nodes of a social network. Content is tagged and the content consumption analytics are derived from the content consumption. The content analytics and related heuristic is applied to new content shared in the social network. The content is compared to policies for content push operations and cellular network constraints to determine whether the content is proactively pushed to a mobile device.