One of more unique products can be selected for advertisement over a digital marketing channel. The selection is based on the calculation of a base impression budget, calculated per product per day, which calculation considers information related to a product, including supply and demand at the market level, characteristics of the property, and popularity of the listing. A real-time, or current, impression budget is calculated to determine whether a particular product should be recommended to a user. Every time the product is advertised to a user, a user intent value is calculated, indicating the user's likelihood of purchasing the product within a given period of time, along with the user intent of every user to which the product has been advertised. The user intent calculation may consider information specific to the user, such as the user's activity history and profile. These user intent values are subtracted from the base impression budget to obtain a real-time impression budget. If the real-time budget is greater than zero, the product will be advertised to a user. By these means, a bound is set on the number of times a product may be advertised before it is assumed to be sold.

A media distribution clearinghouse system is provided with a site selection subsystem having a site table listing a plurality of potential geographic locations, and an interface to accept value weighted geographic location selections and to supply a media enablement signal to a media unit in response to the selected geographic location, so that a media message is displayed at the selected geographic location. The media units may be stationary or mobile. The site table lists locations may be fixed stationary locations, predetermined travel routes, or non-predetermined travel routes. A media message subsystem includes a message table listing a plurality of media messages, and an interface to accept media message selections and supply the media message enablement signal for a selected media message, to the media unit. In some aspect the media message subsystem has an interface to transmit the selected media message to the media unit.

Methods, apparatus, systems, and articles of manufacture to perform computer-based monitoring of audiences of network-based media using information theory to estimate intermediate level unions are disclosed. An example apparatus to determine a deduplicated, census-based audience metric of media includes panel union calculator circuitry to calculate a threshold statistic corresponding to an intermediate union of a panel hierarchy, and census union calculator circuitry to calculate a deduplicated audience value corresponding to the intermediate union of the census hierarchy based on the threshold statistic.

This application relates to apparatus and methods for automatically determining and providing digital recommendations including sponsored items at optimized positions. In some examples, a computing device receives a recommendation request. In response, the computing device obtains a plurality of items based on the user data. The plurality of items includes a plurality of relevant items and a plurality of sponsored items. The computing device then determines a total profit for a combination of the plurality of items by injecting the plurality of promotional items at different position amongst the plurality of relevant items. The plurality of items are presented to the user based at least in part on the total profit and the corresponding combination.

Traditionally, strategies are carried out based on customer intention indicated from surveys, transactional data, location of the store and customer profile. They have limitations such as partial capture of customer intention, absence of transactional data, challenges in defining catchment area associated with store location and lack of customer profiles. This disclosure relates to determining unified user intention from digital environment for plurality of strategies. An information associated with user is received. The information associated with the user is processed to derive weights associated with the user. The weights are integrated and mined through a variable reduction technique to determine at least one significant latent variable from a plurality of significant latent variables and associated significant attribute values. A structural equation model (SEM) is developed by applying the significant latent variables and the at least one event and a plurality of strategies are recommended for the plurality of applications.

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.

Aspects of the subject disclosure may include, for example, receiving, from a first client device, a recommendation pertaining to an advertisement, storing a sender identification in association with an identification of the advertisement, wherein the sender identification includes an identification of the first client device, an identification of a first user of the first client device, or a combination thereof, determining that a score associated with the recommendation exceeds a threshold at least in terms of a likelihood of relevance of the advertisement to a second user of a second client device, based on the determining, transmitting the advertisement to the second client device based on the identification of the advertisement, and based on the determining, transmitting the sender identification to the second client device. Other embodiments are disclosed.

A machine-learning algorithm is trained with features relevant to a modeled set of input directed to patterns of activities specific to a given behavior. The trained algorithm is also trained on success and failures of remediation actions that change or do not change the given behavior. The trained algorithm is then provided the modeled set of input at predefined intervals of time and supplies as output expected deviations/changes that are predicted for the given behavior along with an indication as to whether the remediation actions are likely to prevent or change the expected behaviors.

An information processing apparatus includes a processor configured to from each of a plural order-receiving companies that make-to-order a product ordered by an orderer, receive at least one index value related to a production efficiency of the product and control advertisement distribution to an ordering terminal used by the orderer of the product for an order of the product, so that an advertisement of an order-receiving company having a larger distribution score, that is obtained from the index value for each order-receiving company and indicates a larger value as the production efficiency of the product increases, has a higher degree of contact that the orderer of the product contacts the advertisement.

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.