Methods and systems for generating recommendations are disclosed. In some examples, from a set of positive reviews associated with a merchant offering, at least one attribute is identified and associated with the set of positive reviews, based on reviewer profiles associated with each respective positive review. The attribute is associated with the merchant offering. A match is determined between a customer attribute in a first customer profile and the at least one attribute. A set of recommendations is generated to be presented, via a customer electronic device, to a customer associated with the first customer profile, the set of recommendations including the merchant offering associated with at least one attribute.

Disclosed are techniques for processing user profiles using data structures that are specialized for processing by a GPU. More particularly, the disclosed techniques relate to systems and methods for evaluating characteristics of user profiles to determine whether to offload certain user profiles to the GPU for processing or to process the user profiles locally by one or more central processing units (CPUs). Processing user profiles may include comparing the interest tags included in the user profiles with logic trees, for example, logic trees representing marketing campaigns, to identify user profiles that match the campaigns.

A system for evaluating health risk is provided. The system may be configured for requesting first data from a merchant associated with a user and second data from an activity tracking service associated with the user. The system may also comprise analyzing the item to determine a first health impact for the item based on the item and demographic information associated with the user. The system may further comprise analyzing the level of activity to determine a second health impact from the level of activity based on the level of activity and the demographic information associated with the user. The system may further comprise selecting an advertisement based on medical data, and at least one of the first health impact, the second health impact and the demographic information associated with the user.

An information processing device includes an acquisition unit that acquires user identification information as information identifying a user and position information as at least one item of information out of present position information as information indicating the user's present position and destination information as information indicating a destination, acquires purchase information indicating a purchased item purchased by the user in the past by using the user identification information, and acquires a learned model, an estimation unit that estimates an advertisement to be provided to the user by using the position information, the purchase information and the learned model, and a provision control unit that executes a process for providing the advertisement to the user.

A content compliance system uses machine learning to generate objective verification that content complies with brand criteria. The content compliance system may force the company to select specific brand criteria listed on a user interface. The selected brand criteria are then readily displayed to the creative agency. The content compliance system then compares the selected brand criteria with content generated by the creative agency. The content compliance system uses machine learning algorithms to generate a compliance score that provides a real-time objective indication of the compliance of the creative content with the selected brand criteria. The creative agency can then modify the creative content and receive a real-time updated compliance score.

A classification based notification system is disclosed which performs operations that include: accessing user profile data associated with a user account associated with a client device, the user profile data including a list of user connections; designating a notification type to a portion of the list of user connections, the notification type corresponding to an alert attribute; receiving a notification from a user connection from among the portion of the list of user connections at the client device; and presenting an alert based on the alert attribute that corresponds with the notification type associated with the portion of the list of user connections at the client device.

Systems and methods are described herein for allowing a user to grant or deny a third party access to different portions of the user's profile characteristics. Control circuitry may calculate an optimal promotion offer from a third party to a user based on a first layer of the user's profile characteristics matching a target-audience profile of the third party. Control circuitry may then generate for display the optimal promotion offer to the user. In response to receiving a selection of the optimal promotion offer from the user, control circuitry may receive further selections, from the user, of a subset of the user's profile characteristics to share with the third party, and then generate for display, to the user, an actual promotion offer based on the selected subset of profile characteristics.

A disclosed example includes memory; instructions in the apparatus; and processor circuitry to execute the instructions to: monitor for a mobile device communication at a merchant location; obtain a first identifier from the mobile device communication obtained at a first server in response to identifying the mobile device communication during the monitoring; store the first identifier in association with the merchant location in a database; and in response to determining that the first identifier is present in the database, send an association between the first identifier and the merchant location to a second server of an audience measurement entity, the first identifier included in a second communication from the second server of the audience measurement entity.

Methods and apparatuses are described for automated optimization and personalization of customer-specific communication channels using feature classification. A server captures historical interaction data comprising a channel type, a user identifier, an interaction date, and a user response value. The server generates a channel feature vector for each combination of channel type, user identifier, and interaction date. The server identifies features from the channel feature vectors for each different channel type and aggregates the features into a common feature vector. The server executes a trained classification model on the common feature vectors to select user identifiers for each different channel type that have an engagement probability value at or above a corresponding threshold. The server determines, for each different channel type, a distance value between the engagement probability value and the corresponding threshold and communicates with a remote computing device via a channel that is associated with an optimal distance value.

In an embodiment, a system server receives, from a plurality of advertiser devices, a plurality of advertisements, at least one corresponding personal information value, and a corresponding price. The system server also receives from users ratings of advertisements. The system server calculates, for each of a plurality of personal information types, a corresponding price.