A personalized advertisement and checkout system and method for generating personalized merchant advertisements to specific device users includes a communications network, an advertisement system, at least one merchant administrator operable by a respective merchant user, at least one partner system and at least one user device operable by a respective device user. Each of the advertisement system, the merchant administrator, the partner system and the user device includes a processor and a memory in communication with the processor. Each of the merchant administrator and the user device includes a display and a user interface, the user interface of the merchant administrator receiving input from the respective merchant user and the user interface of the user device receiving input from the respective device user. The advertisement system ranks merchant campaigns entered by the merchant users and generates a list of merchant offer advertisements that is displayed on the user device.

A method of implementing sub-table replication starts with the processor detecting an update to an entitlements table. The processor performs filtering of a data table based on the update to the entitlements table. The data table including an entitlements column. The processor detects an update to the entitlements column and performs incremental replication of the data table by causing a version-based replication to be executed. Other embodiments are also described herein.

A system for sub-table replication within a materialized view of a source data table. A materialized view of a source data table is generated based on a query, the source data table having a plurality of data table rows associated with a plurality of client identifiers and including an entitlements column and data content, the entitlements column including a plurality of provider account identifiers and a plurality of consumer account identifiers. An update to an entitlements table of the source data table is detected where the entitlements table includes a plurality of entitlements table rows and where the entitlements table rows are associated with the client identifiers, the consumer account identifiers, and the provider account identifiers. An update to the source data table based on the first update to the entitlements table is detected, and an incremental replication of the materialized view of the source data table is performed.

A contextual hashtag generation method, system, and computer program product include receiving content from an online source, identifying a set of contextual indicators for the content, determining an entity-desired outcome for the content, and generating a hashtag for the content using the set of contextual indicators while maximizing the entity-desired outcome.

A method is provided that includes receiving, in a server, a request from a service provider, the request including a consumer identification code associated with a consumer, and obtaining a personalized list of universal product codes based on the consumer identification code and a purchase history log in a database. The method also includes providing the personalized list of universal product codes to the service provider, and receiving a tracking pixel indicative that the consumer has interacted with a consumer payload, wherein the consumer payload is associated with at least one product from the personalized list of universal product codes. A system and a non-transitory, computer-readable medium storing instructions which cause the system to perform the above method are also disclosed.

Systems and methods are described for generating efficient iterative electronic recommendation structures. In various aspects, one or more processors aggregate a plurality of ratings vectors, where each ratings vector is associated with a vector type and contains one or more content ranking metrics associated with one or more users. The one or more processors generate similarity pairing values from the plurality of ratings vectors, where each similarity pairing value is based on a similarity mapping between a first ratings vector and a second ratings vector. The one or more processors further generate an electronic recommendation structure based on the similarity pairing values, where the electronic recommendation structure includes a bi-directional look-up interface that is configured to return a bi-directional recommendation value after receiving a lookup request for either the vector type of the first ratings vector or the vector type of the second ratings vector.

This disclosure describes systems and methods for repositioning vehicles. An exemplary method includes obtaining a plurality of first signals corresponding to a vehicle and a plurality of second signals corresponding to supply-demand statuses in a plurality of neighboring areas of the vehicle; inputting the plurality of first and second signals into a trained neural network and obtaining, from the trained neural network, a plurality of action values for repositioning the vehicle to the plurality of neighboring areas respectively; determining, based on the plurality of action values, a plurality of probabilities for repositioning the vehicle to the plurality of neighboring areas respectively; determining, according to the plurality of probabilities, one of the plurality of neighboring areas for the vehicle to reposition to; and transmitting a signal to a computing device associated with the vehicle to reposition the vehicle to the one determined neighboring area.

A service provider may receive item price information and/or point of sale (POS) transaction information from a plurality of merchant devices associated with a plurality of merchants. The service provider may categorize the plurality of merchants according to the types of items offered by the respective merchants, and may further categorize the merchants according to the locations at which they conduct business. The service provider may subsequently determine a price recommendation or other pricing information for a particular item offered by a particular merchant. A merchant device of the particular merchant may present the pricing information in a user interface that enables the merchant to view projected sales at various different price points. Further, the merchant may be able to specify one or more factors used for determining the recommend price, such as a desired business model, a desired clientele, a desired profit margin, or the like.

The present disclosure discloses a method and apparatus for mining offline resources. The method includes: acquiring at least two pieces of user search log information; acquiring a user demand characteristic set according to a search formula included in the user search log information, the user demand characteristic set including a keyword set; and matching the user demand characteristic set with a defined offline resource set, to acquire offline resource information corresponding to the user demand characteristic set.

Systems and methods are provided for use in locating one or more merchant terminals based on transaction data associated with the terminals. One exemplary method generally includes accessing, by a computing device, transaction data for a transaction to a payment account between a merchant and a consumer at a merchant terminal where the transaction data includes a terminal ID for the merchant terminal, a merchant ID of the merchant, an acquirer ID for an acquirer associated with the merchant, and a temporal indicator. In addition, the method includes receiving, by the computing device, location data associated with the transaction, identifying, by the computing device, a location associated with the location data as a location of the merchant terminal, and assigning, by the computing device, a score to the identified location indicative of a confidence that the identified location is the actual location of the merchant terminal.