A recommender engine is configured to access memory and surface transmedia content items; and/or linked transmedia content subsets; and/or one or more identifications of identified users; and/or content items of the plurality of transmedia content items associated with at least one identified user. The surfaced items are presented for selection by the given user via the transmedia content linking engine as one or more user-selected transmedia content items.

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.

A system and method for identifying prospects with a buying intent and connecting them with relevant businesses. An example method may comprise obtaining a training dataset; applying a first scoring algorithm to obtain a first score for each entry in the training dataset; receiving one or more scores from a user for one or more entries in the training dataset; rescoring the training dataset based on the one or more scores received from the user; creating a deep learning model based on the rescored dataset; obtaining digital media posts comprising data from one or more digital media platforms; scoring each received digital media post by using the deep learning model; providing certain scored digital media posts to the user; receiving a second score from the user; and updating the deep learning model based on the second score.

Methods and systems for providing information associated with a location history of a mobile device to one or more applications are disclosed. A mobile device generates one or more location history records based on one or more locations of the mobile device, each location history record comprising one or more points of interest and a duration at the one or more points of interest, receives an information request from at least one application, determines a subset of the one or more location history records that meet criteria from the information request, determines a level of permission for the at least one application based on the information request and the subset of the one or more location history records, and provides information associated with the subset of the one or more location history records to the at least one application based on the level of permission.

Techniques are provided for targeting of online advertisements. Methods are provided which include classifying an individual into a state, of a set of possible states, relative to relationship with regard to an offering or brand. The classification may be based on offline and online information. The states may relate to a degree of favorability with which the individual is disposed with regard to the offering or brand. The individual is targeted with an online advertisement based at least in part on the state into which the individual is classified.

Systems and methods are provided for optimizing displays in one or more user interfaces. An exemplary method may include retrieving web entries from a database and generating a plurality of candidates based on the retrieved web entries, where each web entry of the web entries is a clickable item that is displayed on the one or more user interfaces. Additionally, provide the plurality of candidates for display on the one or more user interfaces and determine click-through rates for each of the plurality of candidates. Thereafter, create a display pool of candidates to display from plurality of candidates based on the click-through rates and update the display pool of candidates responsive to retrieving additional web entries from the database.

Systems and methods for redeeming a reward held by an individual are described. A method for redeeming a reward includes determining threshold criteria for provision of a targeted reward redemption offer, identifying at least one individual based upon the threshold criteria, determining the targeted reward redemption offer, and providing the targeted reward redemption offer to the identified individual. A response to the provided targeted reward redemption offer may be received, and an account of the identified individual may be adjusted in accordance with the targeted reward redemption offer and the received response.

A roadside advertising system includes a roadside display. The system further includes a controller programmed to receive preference and vehicle data associated with one or more vehicles within a predetermined distance of the roadside display and to cause display of an advertisement corresponding to one of a plurality of preferences from the vehicles that maximizes a weighted preference function derived from vehicles within a maximum viewing distance of the roadside display.

Systems are disclosed for determining contextual segments for targeting contextual advertising in metaverses. The system can deploy an observer avatar in a metaverse to capture information inside a portion of a metaverse from behaviors and interactions of a target user avatar. The observer avatar can capture audio, video, images, and text perceived by the observer avatar in the area of the user avatar, along with metadata describing the captured information. Using the captured observations, the system can determine contextual segments classifying the portion of the metaverse. Using a library of the contextual segments, advertisers can distribute contextually targeted advertisements.

A computer-implemented method for enhancing and utilizing user data derived from digital interactions includes receiving user submission data comprising records generated by input into a client side application interface by a first user on a first computing device and transmitted from the first computing device to the aggregation point via at least one of a data transmission service and a data transmission provider. The method includes generating a first dataset associated with the first user, generating a data model based on data in the first dataset and one or more correlations based on attributes in the first dataset, and generating a modeled dataset based on the data model.