An online system accesses a model trained based on a topic associated with a set of content items and the content of the set of content items. The online system applies the model to predict a probability that each of multiple content items is associated with the topic based on its content and identifies (a) content item(s) associated with at least a threshold probability. The online system retrieves information describing user engagement with the identified content item(s) and determines a value of a performance metric for the topic based on this information. If the value is at least a threshold value and the online system receives content from an entity describing an item associated with the topic, the online system communicates a recommendation to the entity to create a content item describing the item and to add a tag associated with the item upon determining an opportunity to do so.

There is disclosed a system and a method for training an MLA to predict a visibility score indicative of a likelihood of a targeted message included within a web resource, the method being executable on a server, the method comprising: generating a training dataset by retrieving a plurality of training targeted messages, the training web resource having a plurality of targeted message slots for placing therein one or more training targeted messages; causing, the training electronic device to display the training web resource; upon the training user accessing the training web resource using the training electronic device during a subsequent instance of time, causing the training electronic device to display the web resource; tracking an activity parameter, the activity parameter being indicative of an interaction by the training user with the given one of the plurality of training targeted messages; generating the training dataset.

A method for shifting transcript costs from a content supplier to a user that includes the steps of transcribing text into a transcription device, transmitting the transcribed text from the stenographic transcription device to a digital store of a computer and providing in the computer an algorithm configured to associate the steno transcribed text with an advertisement for accessing and viewing. The transcribed text is associated with the advertisement and the text and the associated advertisement is delivered to a user's device for display and viewing. The transcribed text may be in document form or captioning displayed for viewing on a video viewing device such as a laptop monitor or television screen.

Provided is a technique for realizing targeting in which social connections are considered. An information processing apparatus acquires factual features of each of a plurality of users as user features, and based on the user features, creates a relationship graph showing a social relationship between the plurality of users, sets one or more users among the plurality of users as a target user group, and based on the relationship graph and the user features of the target user group, adds one or more users, among the plurality of users, having a social relationship with a user included in the target user group to the target user group and determines the resulting target user group as an expanded user group.

This teaching relates to predictive targeting. Training data are obtained with pairs of data. Each pair includes an ad opportunity context corresponding to an ad served to a plurality of audiences and a label vector having a plurality of labels, each of which indicates a reaction, with respect to the ad served, of a corresponding one of the audiences in the ad opportunity context. Based on the training data, model parameters of a joint predictive model are learned via machine learning based on an initialized model with initial model parameters by minimizing a loss in an iterative process. The learned joint predictive model is to be used to map an input context of an ad opportunity to an output label vector having a plurality of probabilities, each of which predicts a likelihood of a reaction of a corresponding one of the audiences to the input context of the ad opportunity.

Systems and methods for machine forward energy and energy credit purchase are disclosed. An example transaction-enabling system may include a machine having an energy requirement for a task and a controller. The controller may include a resource requirement circuit to determine an amount of an energy resource for the machine to service the energy requirement, a forward resource market circuit to access a forward resource market, and a resource distribution circuit to execute a transaction of on the forward resource market in response to the determined amount of the energy resource.

The systems and methods described herein may be used to recommend an item to a consumer. The methods may comprise determining, based on a collaborative filtering algorithm, a consumer relevance value associated with an item, and transmitting, based on the consumer relevance value, information associated with the item to a consumer. A collaborative filtering algorithm may receive as an input at least one of: a transaction history associated with the consumer, a demographic of the consumer, a consumer profile, a type of transaction account, a transaction account associated with the consumer, a period of time that the consumer has held a transaction account, a size of wallet, a share of wallet, and/or the like.

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.

Systems and methods for forward market purchase of machine resources are disclosed. An example transaction-enabling system may include a fleet of machines, each one of the fleet of machines having a resource requirement comprising at least one of a plurality of machine-related resources and a controller. The controller may include an intelligent agent circuit to aggregate data for the plurality of machine-related resources from at least one data source comprising an external data source or an internal data source; an expert system circuit to configure a purchase of at least one of the plurality of machine-related resources; and a machine resource acquisition circuit to automatically solicit the configured purchase of the at least one of the plurality of machine-related resources in a forward market for at least one resource of the plurality of machine-related resources.

The present disclosure describes transaction-enabling systems and methods. A system can include a facility having a core task and a controller. The controller may include a facility description circuit to interpret historical facility parameter values and corresponding outcome values. A facility prediction circuit operates an adaptive learning system to train a facility resource allocation circuit in response to the historical facility parameter values and corresponding outcome values. The facility description circuit further interprets a plurality of present state facility parameter values and the trained facility resource allocation circuit adjusts facility resource values in response.