A system and method for the automatic distribution of rewards for user content within social networks is provided. Content that is posted by users to social networks may display a qualifying product or service, as determined by the operator of the system and method. Such content is identified by software algorithms and evaluated for its marketing value. A quantity of that evaluated value is then paid out to the original user, either immediately or on a deferred basis, in the form of currency. The present invention provides for a novel method to compensate social media users for their unpaid labor they have performed by inadvertently advertising products and services, and thus represents a material benefit for a large segment of the population.

A system may include an aggregate model comprising a plurality of modules, a monitoring dashboard in communication with the aggregate model, and a search-marketing campaign manager in communication with the aggregate model, the monitoring dashboard, and a search engine entity. Additionally, the system may include a bidding interface in communication with the aggregate model and a search engine entity and/or a monitoring dashboard configured to generate model adjustment data. The search-marketing campaign manager may be configured to determine trend data, measured marketing indicators, or both, from one or more of marketing data or historical data.

Disclosed herein is a web user experience improvement for digital magazines. A digital magazine viewing platform is integrated with a digital magazine publishing platform including features that leverage the integration including user interface arrangement based on viewing habits and ripped content that is insertable into draft digital magazine documents. In some embodiments, a machine learning model categorizes magazine styles and present publishing features based on those magazines viewed or subscribed to by a given user.

Aspects of the subject disclosure may include, for example, determining classes from a corpus based on topic modeling, data clustering and unsupervised learning. Labels are determined for each of the classes and trained models are generated for each of the classes by assignment of a plurality of textual documents to labels based on a highest number of matches. A raw textual document can be tokenized and stop words removed. A corresponding one of the trained models can be selected according to a class that is applicable to subject matter of the raw textual document. The processed document can be assigned to a target label based on a highest number of matches of words. Other embodiments are disclosed.

A system and method capable of learning dynamic user and advertisement behavior for more effective click-through rate prediction. The system and method include at least one processor configured to obtain at least one item data, wherein the at least one item data comprises at least one explicit feedback to a user interaction event and other data associated with an item. The at least one processor also uses an interaction model that incorporates the obtained at least one item data to generate a user response prediction for a user and another interaction event.

Embodiments of the present disclosure provide methods, systems, apparatuses, and computer program products for adaptively generating an electronic bid value for a tail digital content object.

A server includes a communicator, a memory configured to store a first neural network model trained to predict winning probability distribution for a bidding price based on advertisement history data of an advertiser and a second neural network model, and a processor configured to, based on receiving a bidding request from an external server, obtain response probability data of a user based on user information included in the bidding request, obtain a first winning probability distribution by inputting data with respect to an auction of an advertiser and the response probability data of the user to the first neural network model and obtain a second winning probability distribution by inputting noise data to the second neural network model, and identify a bidding price based on the first and second winning probability distributions and control the communicator to transmit the identified bidding price to the external server.

One or more computing devices, systems, and/or methods are provided. A machine learning model may be trained using a plurality of sets of information. One or more pruning operations may be performed in association with the training to generate a machine learning model with a sparse set of field weights associated with feature fields associated with features of the plurality of sets of auction information. A request for content associated with a client device may be received. A set of features associated with the request for content may be determined. Positive signal probabilities associated with a plurality of content items may be determined using the machine learning model based upon field weights, of the machine learning model, associated with the set of features. A content item may be selected from the plurality of content items for presentation via the client device based upon the positive signal probabilities.

A computer-implemented method is provided for administering an online advertiser bidding interface. The method includes providing a bidding interface to an advertiser through a web server, by which an advertiser may bid on online advertising inventory of an online publisher, the bidding interface displaying a plurality of targeting elements; receiving targeting information from the advertiser through the targeting elements of the bidding interface and the web server; and generating an advertising bid based on the received targeting information. A system is also provided for administering an online advertiser bidding interface.

Disclosed is an on-line advertisement method using an advertisement website. An on-line advertisement method according to an embodiment of the present invention provides an advertisement available to a user terminal at a current position of a user terminal which accesses an advertisement website.