Examples described herein relate to a system consistent with the disclosure. For instance, the system may comprise a data lake including information relating to an in-store activity of a customer and an online activity of the customer, a processing resource, and a non-transitory machine-readable medium storing instructions executable by the processing resource to identify the in-store activity and the online activity of the customer, aggregate and store the in-store activity and the online activity of the customer in the data lake, reduce an amount of products in a product portfolio, compare purchases including in-store purchases and online purchases, and recommend a product of the plurality of products based on the comparison.

Providing a purchase incentive to a mobile device based for example the user's location, predicted route of travel, and prior transactions. A tracking server computer determines a number of locations of a mobile device as it travels along a route and an associated timeframe when it is at each of the locations. The tracking server computer records the locations and associated timeframes in a location log and analyzes the location log to predict a subsequent location and associated timeframe that the mobile device will be at that location. The tracking server computer then determines a merchant proximate to the predicted subsequent location of the mobile device and generates a purchase incentive for use at the merchant and delivers the purchase incentive to the mobile device. In the alternative, the incentive may be generated by a merchant computer or the mobile device.

Systems and methods are disclosed for protecting user privacy in, for example, online advertising environments. The method includes receiving data related to a user in a first communication session between a host server and a client device, and generating a user profile associated with the user. The method further may include encrypting the user profile to produce encrypted user profile data and generating a decryption key for decrypting the encrypted user profile data. Thereafter, either the decryption key or a portion of the encrypted user profile data may be transmitted to the client device and then deleted from host server before ending the first communication session. The method further may include establishing a second communication session between the host server and the client device and retrieving the transmitted content. Then targeted advertising may be provided by decrypting the encrypted user profile data.

There is provided an apparatus and method for a webshop such that the products shown to a customer are related to the purchasing habits of the customer. A control unit is arranged to communicate with a product information database, a product category database and a customer purchase history database. The control unit includes a product categorising unit arranged to generate at least one product category based on product information in the product information database and to store the at least one generated product category in the product category database. A calculating unit is arranged to calculate a probability of a customer being an underbuyer/overbuyer of a type of product based on the customer's purchase history stored in the customer purchase history database and the at least one product category from the product category database.

A system and method for scalable, adaptive, real-time generation of personalized offers is disclosed. A profile of a user is generated, the profile being a summarized representation of historical behavior of the user, the profile containing recursively updated variables. The profile is updated for each new transaction and/or a time dependent event, the new transaction and/or time dependent event including purchase transaction data, user, item hierarchy, and offer data. A affinity scores is generated for the user based on the updated profile, and for each new transaction, one or more offers are generated for the user based on the updated user profile and the affinity scores.

A distributed computing device stores user preference data representing preferences of a user with respect to a portion of a set of items. The distributed computing device randomly samples the user preference data to calculate sampled user preference data. The distributed computing device iteratively executes, in conjunction with additional distributed computing devices connected by a network, a process to determine a consensus result for the sampled user preference data. The consensus result is based on the sampled user preference data and additional sampled user preference data calculated by the additional distributed computing devices and based on preferences of additional users. The distributed computing device determines a recommendation model based on the consensus result, the recommendation model reflecting preferences of the user and additional users. The distributed computing device identifies an item to provide to the user as a recommendation based on the recommendation model, and provides recommendation to the user.

A virtual repository system with robust item management may automatically derive item data from accessed current and past transactions. The system may interface with merchant systems to receive current and archived transaction data, scans emails for current and past transaction data, monitors browser data for online transaction data, and accepts manual input. Data obtained from all sources is collated and stored in a cache for user validation, whereupon it is added to a virtual repository. Triggers prompt the delivery of responsive results including information from shared virtual repositories. Triggers may include video or sound, from which item records may be identified and reported to an inquiring user.

A system and method directed at business administration are provided which may be implemented with an application executed on a computing device. A processor comprising the computing device may be operative to register and assign roles to various system users and grant users access to a plurality of administration modules according to each user’s role and in response to receiving a single set of user-authenticating credentials. The plurality of administration modules may comprise a combination of communication, billing, service, marketing and analytics, and task modules.

Social media networking applications, web sites, and services creates implicit relationships between users based on their interest or participation in real-world and optionally virtual or online activities in addition to explicitly defined peer relationships. User profiles, activity entities, and expressions may be associated with metadata to assist in searching and navigation. Metadata is implicitly associated with user profiles, activity entities, expressions, or other data entities based on user behavior using metadata collector. A metadata collector is a poll, survey, list, questionnaire, census, test, game, or other type of presentation adapted to solicit user interaction. A metadata collector is associated with metadata elements. When users interact with a metadata collector, their user profiles and the data entities included in their interactions become associated with the metadata elements of the metadata collector. These metadata element associations may then be used for any purpose.

A cryptographic anonymization method, apparatus, and system are disclosed. An example apparatus includes a server configured to receive encrypted usage information and an identifier from an application operating on a user terminal and trans-cypher the encrypted usage information from a first encryption scheme to a second encryption scheme to create second encrypted usage information without decrypting the encrypted usage information. The server is also configured to convert and encrypt the identifier to an encrypted unique identifier. The server is further configured to compare the second encrypted usage information to a taxonomy of data labels using rules. For each match of at least some of the second encrypted usage information to a data label, the server is configured to add the encrypted unique identifier to the matching data label. The server uses the data labels and/or the encrypted unique identifier for serving advertisements to the user.