Embodiments of this application provide a for training an information recommendation model. The method includes: obtaining historical user behavior data in a plurality of product domains; generating candidate sample data of one or more target product domains according to the historical user behavior data by using a generative model; performing user-specific authenticity sample discrimination on candidate sample data of the target product domains and actual user click sample data by using a discriminative model, to obtain a discrimination result; and performing adversarial training on the generative model and the discriminative model according to the discrimination result, to obtain a trained generative adversarial network as an information recommendation model for a to-be-expanded product domain in the plurality of product domains. According to the method, the training effect of the generative model may be improved, the accuracy of generating the pseudo sample is improved, thereby further improving the recommendation effect.

The present disclosure describes transaction-enabling systems and methods. A system can include a facility including a core task including a customer relevant output and a controller. The controller may include a facility description circuit to interpret a plurality of historical facility parameter values and corresponding facility outcome values and a facility prediction circuit to operate an adaptive learning system, wherein the adaptive learning system is configured to train a facility production predictor in response to the historical facility parameter values and the corresponding outcome values. The facility description circuit also interprets a plurality of present state facility parameter values, wherein the trained facility production predictor determines a customer contact indicator in response to the plurality of present state facility parameter values and a customer notification circuit provides a notification to a customer in response.

Aspects described herein may allow for generating a customized price rating using a machine learning algorithm. This may have the effect of improving the display of information about merchants by including customized, personalized price ratings that better reflect the tastes and preferences of a user or group of users. According to some aspects, these and other benefits may be achieved by using a machine learning model, trained to receive input corresponding to both user data and merchant data and output an indication of a customized price rating for the merchant that is specific to the user, and then to generate information about the merchant for display that includes the customized price rating.

A method, apparatus, and computer program, each configured for determining a travel propensity, the travel propensity being a distance a customer is willing to travel to redeem a renderable data object, are provided An exemplary method comprises classifying each of a plurality of geographic regions as one geographic region type of a plurality of geographic region types, mapping each of a plurality of redemption locations to a geographic region type, calculating, for a first geographic region type, a first distance that accounts for a predefined number of redemptions, determining a number of merchants having redemption locations within the first distance, calculating a second distance, from a center of a geographic region of the second geographic region type, necessary to include the number of merchants having redemption locations within the first distance, and assigning the second distance as the second geographic region type travel propensity.

A method and an apparatus for predicting satisfaction of a customer pursuant to a call between the customer and an agent, in which the method comprises receiving a transcribed text of the call, dividing the transcribed text into a plurality of phases of a conversation, extracting at least one call feature for each of the plurality of phases, receiving call metadata, extracting metadata features from the call metadata, combining the call features and the metadata features, and generating an output, using a trained machine learning (ML) model, based on the combined features, indicating whether the customer is satisfied or not. The ML model is trained to generate an output indicating whether the customer is satisfied or not, based on an input of the combined features.

Systems and methods are provided for determining in real-time geographic areas having a threshold level of content consumption and deploying dynamic geo-fences to contain these geographic areas. These dynamic geo-fences provide a means for timing message notifications in order to optimize the chances of delivering targeted content to a mobile device user based on the current geographic location of the user's device relative to a threshold level of content consumption area. As mobile device users may be more likely to launch a client application in a place where other users are currently consuming content, a general message notification sent to the user's device located in a dynamic geo-fence created based on real-time content consumption, may increase the likelihood that the user will launch the client application and thereby, allow targeted content to be delivered to the user's mobile device.

The present disclosure provides methods and systems for quantifying item performance in a digital shelf. A method for quantifying item performance in a digital shelf may comprise: calculating a value associated with a shelf share of the given item; determining a set of factors for calculating a score indicative of the item performance on the digital shelf, wherein the set of factors includes the shelf share; generating, using a trained machine learning algorithm, the score based on the set of factors; and displaying the score within a graphical user interface (GUI) on an electronic device.

Certain aspects of the disclosure are directed to context aggregation in a data communications network. According to a specific example, user-data communications between a client-specific endpoint device and the other participating endpoint device during a first time period can be retrieved from a plurality of interconnected data communications systems. The client entity can be configured and arranged to interface with a data communications server providing data communications services on a subscription basis. A context can be determined for each respective user-data communication between the endpoint devices during the first time period. A plurality of user-data communications between the client-specific endpoint device and the other participating endpoint device can be aggregated during a second time period, and a context can be determined for the aggregated user-data communications during the second time period based on a comparison of the aggregated user-data communications and the user-data communications during the first time period.

A computer implemented system for an intelligent timeline includes computer readable instructions to operate a timeline engine, a logic extraction and application engine, a calendar engine, a performance evaluation engine, an advertisement placement engine, and a social networking engine that are interconnected to one another. The timeline engine creates a timeline of events containing external events and/or an owner's actions. Each event has a timestamp such that the events may be arranged in the order of timestamps. The logic extraction and application engine extracts the logical inferences from the events to be used by the timeline engine. The calendar engine creates a calendar containing the events and other reminders. The performance evaluation engine creates performance evaluation results of an owner's actions based on the events. The timeline of an owner may be sold or shared on the owner's social networking channel to subscribers. Advertisement placement engine facilitates advertisement transactions related to the timelines.

A computer implemented system for an intelligent timeline includes computer readable instructions to operate a timeline engine, a logic extraction and application engine, a calendar engine, a performance evaluation engine, an advertisement placement engine, and a social networking engine that are interconnected to one another. The timeline engine creates a timeline of events containing external events and/or an owner's actions. Each event has a timestamp such that the events may be arranged in the order of timestamps. The logic extraction and application engine extracts the logical inferences from the events to be used by the timeline engine. The calendar engine creates a calendar containing the events and other reminders. The performance evaluation engine creates performance evaluation results of an owner's actions based on the events. The timeline of an owner may be sold or shared on the owner's social networking channel to subscribers. Advertisement placement engine facilitates advertisement transactions related to the timelines.