The disclosure relates to securing and enabling user control of profiling data, blockchain-driven matching of users and advertiser-identified anonymous profiling data records of interest, and smart contracts encoded by blockchain for executing transactions. The system may include an anonymized database of profiling data, which is unlinked to any user. The system may implement a private blockchain to store user-defined settings that provide user control over whether and how the profiling data may be used. If a grant to use the data is received, a link is stored that allows the system to identify a user associated with the anonymous profiling data records. If the grant is revoked, the link may be removed. The system may also implement public blockchain technology to record a public information relating to grants, online marketing transactions, making them verifiable, immutable, and transparent for various stakeholders including advertisers, publishers, and users.

The present disclosure relates to systems, non-transitory computer-readable media, and methods for generating and utilizing a touchpoint attribution attention neural network to identify and measure performance of touchpoints in digital content campaigns. For example, a deep learning attribution system trains a touchpoint attribution attention neural network using touchpoint sequences, which include user interactions with content via one or more digital media channels. In one or more embodiments, the deep learning attribution system utilizes the trained touchpoint attribution attention neural network to determine touchpoint attributions of touchpoints in a target touchpoint sequence. In addition, the deep learning attribution system can utilize the trained touchpoint attribution attention neural network to generate conversion predictions for target touchpoint sequences and to provide targeted digital content over specific digital media channels to client devices of individual users.

The present disclosure describes transaction-enabling systems and methods for enabling machine resource transactions. A system can include a machine having at least one of a compute task requirement, a networking task requirement, and an energy consumption task requirement; and a controller. The controller can include a resource requirement circuit to determine an amount of a resource for the machine to service task requirement, a resource market circuit to access a resource market, and a resource distribution circuit to execute a transaction of the resource on the resource market in response to the determined amount of the resource.

Systems and methods for forward market price prediction and sale of energy storage capacity are disclosed. An example transaction-enabling system may include a fleet of machines having an aggregate energy storage capacity; and a controller, comprising: an external data circuit structured to monitor an external data source and collect data from the external data source; an expert system circuit structured to predict a forward market price for energy storage capacity based on the collected data and the aggregate energy storage capacity; and a smart contract circuit structured to automatically sell at least a subset of the aggregate energy storage capacity on a forward market for energy storage capacity in response to the predicted forward market price.

The present teaching relates to method, system, and programming for evaluating expressions. An expression indicative of conditions and metadata associated therewith is obtained. A determination is made as to whether the expression corresponds to a modified version of an earlier expression based on the metadata. In response to a determination that the expression is the modified version of the earlier expression, a query associated with the modified expression is transmitted to a forecasting cluster so that the modified expression is to be evaluated by the forecasting cluster. In response to a determination that the expression does not have a corresponding earlier expression, the expression is evaluated.

Automatic item placement recommendation is described. An item placement configuration system receives an item for which a recommended placement is to be generated and identifies an entity associated with the item. The item placement configuration system then identifies a multi-domain taxonomy that describes relationships between different entities based on items associated with the different entities published among different domains. A representation of the entity associated with the item to be placed is then identified within the multi-domain taxonomy, along with a representation of at least one similar entity. Upon identifying a similar entity, historic item placement metrics for the similar entity are leveraged to generate a placement recommendation for the received item. In some implementations, the placement recommendation is output with a visual indication of a similar entity and associated performance metrics that were considered in generating the recommended placement.

A system for maintaining product networks to provide recommendations for bid amounts to third party entity devices. A service provider entity device tracks product purchase history for customers and determines relationships associated with products purchased by the customers. When a webpage is loaded by a customer device, a third party entity device receives a bid request from a publisher device and transmits a bid recommendation request to the service provider entity device. The service provider entity device determines input features including a base bid, a propensity score, and a pacing score based on the relationships. The service provider entity device transmits a bid recommendation response with a recommended bid amount to the third party entity device, based on the input features.

Machine-learning based multi-step engagement strategy modification is described. Rather than rely heavily on human involvement to manage content delivery over the course of a campaign, the described learning-based engagement system modifies a multi-step engagement strategy, originally created by an engagement-system user, by leveraging machine-learning models. In particular, these leveraged machine-learning models are trained using data describing user interactions with delivered content as those interactions occur over the course of the campaign. Initially, the learning-based engagement system obtains a multi-step engagement strategy created by an engagement-system user. As the multi-step engagement strategy is deployed, the learning-based engagement system randomly adjusts aspects of the sequence of deliveries for some users. Based on data describing the interactions of recipients with deliveries served according to both the user-created and random multi-step engagement strategies, the machine-learning models generate a modified multi-step engagement strategy.

A system and method for dynamically tracking open slots that become available during looped digital content on a non-personal digital display and dynamically inserting programmatic content into the open slots by first ranking the programmatic content based on a number of parameters.

Methods and apparatus to generate corrected online audience measurement data are disclosed. An example apparatus includes programmable circuitry to at least receive, from a server of a database proprietor, a first audience count, a second audience count, and a third audience count, the first audience count indicative of a first number of impressions corresponding to first network communications, the second network communications from second computing devices, and the third audience count indicative of a third number of impressions corresponding to ones of the first network communications and ones of the second network communications attributed to the first media category and the second media category accessed by the first demographic group, the first audience count including a server-generated duplicated audience count generated by the server of the database proprietor, and calculate a deduplication factor for the first demographic group using the first, second, and third audience counts.