Methods, computer program products, and systems are presented. The methods include, for instance: receiving, by one or more processor, floor area data, zone data, trigger interaction data, and notification data associated with the products offered for sale in the different zones. The method can include receiving, by the one or more processor, triggering event data regarding a product or products in response to user interaction with a mobile device in the venue and location data of the mobile device in the venue at the time of the triggering event data, automatically determining whether the received triggering event data and the received location data match an associated zone data and trigger interaction data, and automatically sending an associated notification to the mobile device regarding purchase of a product or products in the venue.

The systems and methods may be used to recommend an item to a consumer. The methods may comprise determining, based on a collaborative filtering algorithm, a consumer relevance value associated with an item, and transmitting, based on the consumer relevance value, information associated with the item to a consumer. A collaborative filtering algorithm may receive as an input a transaction history associated with the consumer, a demographic of the consumer, a consumer profile, a type of transaction account, a transaction account associated with the consumer, a period of time that the consumer has held a transaction account, a size of wallet, and/or a share of wallet. The method may further comprise generating a ranked list of items based upon consumer relevance values, transmitting a ranked list of items to a consumer, and/or re-ranking a ranked list of items based upon a merchant goal.

The method includes: collecting historical operations of sample users for M items, and predicting a preference value of a target user for each of the M items according to historical operations of the sample users for each of the M items, collecting classification data of N to-be-recommended items, and classifying the N to-be-recommended items according to the classification data of the N to-be-recommended items, to obtain X themes, where each of the X themes includes at least one of the N to-be-recommended items, and the N to-be-recommended items are some or all of the M items; calculating a preference value of the target user for each of the X themes according to a preference value of the target user for a to-be-recommended item included in each of the X themes; and pushing a target theme to the target user.

Propensities of entities, comprising human users and virtual assistants (VAs), for various behaviors can be determined and used to facilitate managing interactions between entities. An interaction management component (IMC) can determine an aggregate propensity metric relating to a propensity of an entity to engage in a behavior based on a cross-correlation of respective propensity metrics relating to respective propensities of the entity to engage in respective behaviors. During an interaction between entities, including the entity, IMC can determine an action to perform to interact with the entity based on the aggregate propensity metric and a context determined for the interaction. The action can be one that is predicted to elicit a defined action by the entity in response to the action. During (or after) the interaction, IMC can update behavior attributes, context, and/or aggregate propensity metric associated with the entity based on actions performed during the interaction.

Methods, computer-readable media, systems, and/or apparatuses for providing offer and insight generation functions are provided. For instance, user input may be received requesting generation of an offer. In response to receiving the request, an application may be transmitted to a device, such as a mobile device of a user. In some examples, the application may be executed by the device and may facilitate establishing a communication session with a third party system, identifying and extracting data from the third party system, and transmitting the extracted data to an entity for evaluation. In some examples, evaluation by the entity may include generating one or more insights, outputs and the like. In some arrangements, the evaluation may be performed using machine learning and, in some examples, may be performed in real-time or near real-time.

Provided are various mechanisms and processes for generating dynamic merchant similarity predictions. In one aspect, a system is configured for receiving historical datasets that include a series of merchants from historical browsing sessions generated by one or more users. The merchants are converted into corresponding vector representations for training a predictive model to output associated merchants based on a generated weighted vector space. Once sufficiently trained, data from a new browsing session may be received, which may include a target merchant. The target merchant is input into the predictive model as a vector to output one or more context merchants having vectors with the highest cosine similarity value to the target merchant vector. Selected context merchants may then be transmitted to the user device as targeted merchant suggestions in the new browsing session. The predictive models may be continuously trained using data received from subsequent browsing sessions.

One variation of a method for selectively advertising items in an image includes: loading an image to a social feed; receiving a first tag and a second tag including identification of a first item and a second item visible in the image, respectively; based on the first tag and the second tag, correlating the first item with a first product and the second item with the second product; based on the first product and the second product, sourcing a first link to a first electronic storefront and a second link to a second electronic storefront that facilitate purchase of the first product and the second product, respectively; and selectively displaying a first visual cue of the first link and a second visual cue of the second link to a first user and to a second user, respectively, according to demographics of the first user and the second user.

Systems and methods are described for determining usage information. A computing device may determine an advertising event associated with content. The computing device may cause activation of a data capture component to capture data at one or more times associated with the advertising event. The data can be analyzed to determine usage information indicative of user behavior during the advertising event.

Systems and methods for incorporating intelligent virtual assistants into advertisements on social networking platforms are provided. When a user interacts with a content item, an intelligent virtual assistant is selected and put into contact with the user. The intelligent virtual assistant is provided with a context that includes information about the user in the social networking platform, information about the user in a customer relationship management platform, and information about the product, service, or entity associated with the content item. The context allows the intelligent virtual assistant to converse with the user in a way that feels natural and relevant to the user and allows the intelligent virtual assistant to answer any questions about the product, service, or entity associated with the content item.

The present disclosure relates to methods, systems, and apparatuses for providing promotion recommendations using a promotion and marketing service. Some aspects may provide a method for providing a promotion recommendation framework. The method includes receiving, via a network interface, a promotion recommendation inquiry from a component of a promotion and marketing service, the promotion recommendation inquiry including electronic identification data identifying at least one of a consumer or a consumer characteristic. The method also includes identifying, via processing circuitry, promotion transaction information associated with the electronic identification data. The promotion transaction information includes electronic data identifying at least one transaction performed using the promotion and marketing service. The method also includes determining, via the processing circuitry, at least one promotion recommendation based on the promotion transaction information, and providing, via the network interface, the at least one promotion recommendation in response to the promotion recommendation inquiry.