The present invention relates to summarizing cross-network user behavioral data. The summarizing cross-network user behavioral data may particularly include publishing the data to one or more data structures that become accessible to a server hosting an authorized domain when a user accesses the authorized domain.

A computer-implemented method for managing a campaign within a distributed computer network comprises acts of defining, in a memory of the distributed computer network, a personalized URL (pURL) associated with at least one user-specific advertisement; receiving trigger instructions that identify one or more conditions under which at least one second user-specific advertisement should be sent, wherein the second advertisement is associated with the pURL; sending the pURL associated with the at least one user-specific advertisement; tracking the pURL associated with the at least one user-specific advertisement for at least one trigger defined by the trigger instructions; receiving the trigger; and updating a campaign workflow responsive to the received trigger.

An engagement time computer system for serving online content based on user engagement duration includes an output device, at least one memory, and one or more processors. The processors are configured to determine a historical engagement time of an online user and to identify a plurality of online content items. Each online content item of the plurality of online content items has a target engagement time. The processors are further configured to receive a request for a content item. The request is associated with the online user. The processors are also configured to select a first online content item from the plurality of online content items based at least in part on the historical engagement time of the online user and the target engagement time of the first online content item and to transmit the first online content item for display to the online user.

Application personalization techniques and systems are described that leverage an embedded machine learning module to preserve a user's privacy while still supporting rich personalization with improved accuracy and efficiency of use of computational resources over conventional techniques and systems. The machine learning module, for instance, may be embedded as part of an application to execute within a context of the application to learn user preferences to train a model using machine learning. This model is then used within the context of execution of the application to personalize the application, such as control access to digital content, make recommendations, control which items of digital marketing content are exposed to a user via the application, and so on.

A skin care computing device that includes a user interface, a camera, a speaker, a communication interface, and a personal care assistant application. The personal care assistant application can identify personal care products being used by a user and product use event data describing the use of a personal care product by the user, such as the date and time of the use, the duration and frequency of the use, and the manner in which the personal care product is being used. Then the personal care assistant application generates user feedback information for the personal care product based on the product use event data, previous instances of product use event data, and/or user profile data for the user.

Provided are various mechanisms and processes for generating dynamic merchant scoring predictions. A system is configured to receive datasets comprising pairings between training customer profiles and training merchant profiles. For each pairing, a set of feature values corresponding to features specified by the customer and merchant profiles are extracted and converted into a training vector to train a machine learning model to determine a weighted coefficient for each feature. Once sufficiently trained, the system determines a set of available merchant profiles for a customer profile in response to receiving a search request from a customer associated with the customer profile. For each pairing between the customer profile and an available merchant profile, the system determines an order score for the available merchant based on the weighted coefficients and an input set of feature values specified by the customer profile and the available merchant profile.

Methods and systems classify and cluster invoice data. An invoice is obtained. A category vector is generated from an invoice string of the invoice with a dense layer of a machine learning model that includes an embedding layer, a neural network layer, and the dense layer. A suggestion is selected with a selection engine and in response to comparing the category vector to a set of clusters. The suggestion is presented.

Systems and methods for electronic data privacy, consent, and control in electronic transactions are provided. The system includes a customized software application executing on a computing device which provides the electronic data privacy, consent, and control functions in order to allow a user to control the dissemination and usage of PII during an electronic transaction with a third party such as a vendor, service provider, manufacturer, advertiser, etc. The system provides a single platform whereby the user only needs to provide his/her PII once, and the usage and control of such information is administered by the system. The system significantly increases the speed and efficiency with which electronic transactions are processed, and also significantly reduces data privacy risks associated with having to supply PII on multiple portals.

The present disclosure provides a computer-implemented method and system for running high performance marketing campaigns for granular-level segments of users in real-time. The computer-implemented method and system corresponds to a user segmentation system. The user segmentation system receives a first set of data associated with a plurality of users. The user segmentation system fetches a second set of data. The user segmentation system obtains a third set of data. The user segmentation system analyzes the first set of data, the second set of data and the third set of data. The user segmentation system enables segmentation of the plurality of users. The user segmentation system assigns one or more segment goals. The user segmentation system creates a plurality of micro-segments. The user segmentation system triggers initialization of one or more marketing campaigns. The user segmentation system predicts performance of each of the one or more marketing campaigns.

A media enhanced shopping cart system comprises a shopping cart, a locationing component, and a display component for displaying at least one advertisement for a product based on the location of the shopping cart within the store, wherein the locationing component is further operable to determine a location of the product within the store relative to the shopping cart, and wherein the display component is further operable to display an indication of the location of the advertised product relative to the location of the shopping cart.