A method for identifying a plurality of related computing devices related to a transaction account includes: storing token profiles, each including data related to a payment token including a digital token number, associated transaction account number, computing device identifier, and computing device data; receiving a transaction data entry related to an electronic transaction including transaction data and one of: a specific transaction account number or an associated specific digital token number; identifying a plurality of related token profiles where the included associated transaction account number corresponds to the specific transaction account number; identifying a third party entity associated with at least one identified token profile; and electronically transmitting the transaction data included in the transaction data entry and at least one of: the computing device identifier and computing device data included in each of the identified plurality of related token profiles to the third party entity.

A server may be utilized as a content management server for receiving, validating, and formatting an object for use by a second server. The server may receive a selection of a digital communication message type, receive a set of content data corresponding to the selected message type and an indication of one or more target segments. Based on the selected communication message type, the server may validate the data. If the data is validated, then the server generates a communication message content payload object. The communication message content payload object is formatted based on the requirements of the second server, such that the second server may efficiently process the objects for distribution of communication messages using the data of the object. The second server may generate and transmit previews of messages to the first server, for review and approval by an authorized user.

A content scheduling method is provided. The content scheduling method, which is performed by a content scheduling apparatus, comprises acquiring a total play count of target content, determining a plurality of weight values of the target content with respect to a plurality of time slots, each weight value of the plurality of weight values indicating a first preference for the target content with respect to each time slot of the plurality of time slots, generating a linear programming model using the acquired total play count and the plurality of weight values and determining, via a processor, a play count of the target content in the each time slot of the plurality of time slots based on the linear programming model.

A system can access data about items for a store and generate a plan for configuring the store based on the accessed data. The system can further provide, for example, information for configuring reconfigurable shelving units so as to produce the layout of the store according to the plan.

At least some embodiments are directed to a system that receives data generated when point of sale terminals are used. The point of sale terminals are associated with multiple merchants. The system generates a quantitative data from the received data. The system computes a quality of service score of a first merchant based on the quantitative data. The system generates an item data structure storing values that correlate multiple users with multiple merchants at least based on the quality of service score. The system determines users that are shared by multiple merchants and computes a customer relevance value associated with the user and the first merchant. The system outputs a user-specific recommendation associated with a second merchant to a user computing device, wherein the user-specific recommendation is computed at least in part based on the customer relevance value associated with the first merchant.

A method and apparatus for optimizing an Ad CTR estimation model are provided. The method includes: calculating a direction vector and a step vector based on data in a training set, wherein the direction vector and the step vector are associated with a first parameter vector, and the first parameter vector is a parameter vector of the Ad CTR prediction model; calculating an optimized first parameter vector by setting the first parameter vector, the direction vector and the step vector as inputs of an update function, and by using a second parameter vector, wherein the second parameter vector is a parameter vector of the update function; estimating an optimized second parameter vector according to an optimization target in a validation set, the optimization target is determined by using the optimized first parameter vector; updating the optimized first parameter vector by using the optimized second parameter vector.

Disclosed are systems and methods for deploying a targeted advertisement. The systems and methods may include identifying a time period of expected decreased traffic. The targeted advertisement may be generated based on constraints set by a user. Targeted advertisement may be deployed during a predetermined time period prior to the time period of expected decreased traffic.

A recommendation system and method access a recommendation bundle pool including multiple recommendation algorithms, each of which is capable of generating one or more recommendations. A first recommendation bundle comprising two or more recommendation algorithms is selected from the pool. Using the first recommendation bundle, recommendations are generated to provide to visitors to a website. When the recommendation system detects a triggering condition for a scaling cycle, the recommendation system applies a scaling mechanism to increase an exploration of additional recommendation bundles from the recommendation bundle pool. Based on the exploration, the recommendation system selects a second recommendation bundle including algorithms selected from the pool. Recommendations are generated using the second recommendation bundle.

This specification describes technologies relating to content presentation. In general, one aspect of the subject matter described in this specification can be embodied in methods that include the actions of receiving ad information from a seller; generating, using one or more processors, a single conversion ad using the received ad information, where the single conversion ad has an available inventory of one such that only a single conversion of the ad can be performed; transmitting the single conversion ad to one or more potential buyers; receive an input from one of the one or more potential buyers; and notifying the seller of the user input. Other embodiments of this aspect include corresponding systems, apparatus, and computer program products.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for data processing and content distribution. In one aspect, a method includes providing skippable content items from a given content category to a user device; determining, for the user, an aggregate engagement level with the skippable content items based on portions of the skippable content items that were presented and whether the skippable content items were skipped; determining, for the user, an interest score for the given category based on comparing the aggregate engagement level to a population engagement level with skippable content items from the given content category; determining that the interest score of the user for the given category is below a specified threshold; and in response to receiving a subsequent request for a content item from the given content category distributing, to the user device, a non-skippable content item from the given category.