In examples provided herein, a method comprises calling a demographic package to analyze data associated with electric toll tags used on a toll road along which a digital billboard is positioned to determine time-dependent demographics of drivers of vehicles driven on the toll road. The method also includes calling an advertisement selection package to identify an advertisement to be displayed on the digital billboard, where the advertisement to be displayed is based on the time-dependent demographics of the toll road. Further, the method comprises transmitting the advertisement to be displayed to the digital billboard.

The present disclosure provides systems and methods for entropy balanced population measurement. Entropy balancing is a statistical technique for preprocessing data to achieve covariate balance. Weighting coefficients may be dynamically adjusted to satisfy balance conditions or constraints to adjust for inequalities in representation, thereby improving covariate moments. Using entropy balancing and linear regression analysis with panel content exposure and results data provides a mechanism to estimate the effects of multiple exposure vectors simultaneously, including instances where panelists are exposed to a vector multiple times. Data may be obfuscated or anonymized for preprocessing via a double-encrypted intersection-based extraction mechanism, allowing both measurement systems and panel providers to retain confidential information.

Methods and systems are provided that may be utilized to detect occurrence of one or more specified circumstances. A determination may be made as to whether one or more specified circumstances are detected such as responsive to one or more user actions or an occurrence of an event unrelated to a user. One or more binary digital signals may be generated to store a detection of one or more specified user circumstances in a log or memory at least partially in response to detection of the one or more specified circumstances.

An online system receives a request from an online system user to present a content item associated with an action that may be performed on a third party website not associated with the user. The online system identifies a set of third party websites on which the action may be performed based on information provided by content publishers associated with the websites describing performances of the action on the websites. The online system predicts a likelihood a viewing user of the online system presented with the content item will perform the action on each third party website based on the information provided by the content publishers and selects a website associated with a highest predicted likelihood the viewing user will perform the action on the website. The online system generates the content item including a link to the selected website and provides the content item for presentation.

A method, system and computer program product for presenting content (e.g., advertisements) to vehicle occupants. An image of the occupants of the vehicle is obtained. The vehicle occupants, such as the driver, are then identified from the image. After obtaining the driving history of the driver, which includes any information that indicates a risk posed to the driver in not paying attention to the road, a risk degree of the driver getting into an accident from viewing content on the electronic billboard is assessed. A “watch ability” of the vehicle occupants to watch content is calculated, based, at least in part, on the driver's risk degree. If the value of the watch ability exceeds a threshold value, then the content preferences of the vehicle occupants are obtained which are used to determine the contents to be presented on the electronic billboard which are targeted to the vehicle occupants.

A user identifies products offered by the user to an online system. The online system identifies a product offered by the user in an image by applying a identification model to the image. If the online system identifies a product in the image with at least a maximum confidence value, the online system automatically tags the post with metadata about the product or suggests a tag to the user. If an object in the image could be one of multiple products, the online system identifies the multiple products to the user, which may be ordered based on confidences of matching the object, allowing the user to select which product is in the image. If the unlisted identifies a product in the image with less than a minimum confidence value, the online system identifies the user's offered products and suggests that the user select a product.

A financial institution computing system includes an account database structured to store a plurality of transaction parameters with respect to a financial account of a customer, a receptiveness metrics circuit, and an interaction generation circuit. The receptiveness metrics circuit is structured to extract the plurality of transaction parameters from the account database. The receptiveness metrics circuit is further structured to determine one or more receptiveness metrics attributed to the customer based on the extracted transaction parameters, the one or more receptiveness metrics indicating likelihoods of the customer converting an interaction from the financial institution. The interaction generation circuit is structured to transmit the interaction to the customer at an optimal time based on the one or more receptiveness metrics.

This application relates to apparatus and methods for automatically determining and providing item category advertisement recommendations. In some examples, a computing device obtains transaction data identifying historical transactions. The computing device generates a first model, and trains the first model with non-seasonal data. The computing device generates a second model, and trains the second model with seasonal data. The computing device then generates a seasonal re-rank model based on the first model and the second model. The seasonal re-rank model, when executed, identifies probabilities of purchase of categories of items. In some examples, the computing device selects item advertisements to provide for display to a customer based on the probabilities of purchase of categories of items determined by the seasonal re-rank model. The selected item advertisements may be displayed to the customer, for example, on a website.

Embodiments are disclosed for automated evaluation of compatibility of a data structure with a user device. An example method includes receiving, by communications circuitry, a set of user device characteristics regarding the user device, and retrieving, by the personalization circuitry, a set of data structure characteristics regarding the data structure. The example method further includes calculating, by the personalization circuitry, a set of characteristic-level compatibility scores, and generating, by the personalization circuitry and based on the set of characteristic-level compatibility scores, a compatibility score for the data structure and the user device. Subsequently, the example method includes generating, by an aggregator and using the generated compatibility score, an indication of relative compatibility of the data structure for the user device, and causing transmission, by communications circuitry, of a control signal to the user device based on the indication of relative compatibility. Corresponding apparatuses and computer program products are also provided.

Techniques are described herein for intelligently identifying and creating user segments based on public data attributes in addition to user data attributes. In certain embodiments, a data analytics platform is disclosed that receives user data associated with users and public event streams from public data sources. The data analytics platform selects a set of public event categories associated with the public event streams to display in a segment builder application for generating user segments associated with the users. The data analytics platform outputs the set of public event categories and the user data associated with the users to enable a user of the segment builder application to generate the user segments based on the user data and the set of one or more public event categories.