Abstract: Some embodiments provide systems to provide metadata abstraction for a comparison interface. An example system includes a non-transitory machine-readable medium storing instructions that, when executed by a processing resource, may cause the resource to: compile textual metadata associated with an item, determine a ranked list of use cases for the item based in part on using one or more prompts to a language model, at least one of the one or more prompts includes the textual metadata, receive, via a client interface provided to a client device, a selection of the item and a comparison item; and select a highlighted use case to display with the item in a comparison interface of the client interface, wherein the highlighted use case is selected based on the ranked list associated with the item and a ranked list of use cases associated with the comparison item in the use case database.

Abstract: Examples may be related to cross-category recommendation. An example may involve identifying an anchor item to be presented to a user via a user interface, wherein the anchor item is in a first category; and evaluating, using a machine learning model, a degree of cross-category intent of the user based at least partially on real-time interaction data of the user. The degree of cross-category intent may indicate a likelihood that the user will engage with any item in a second category that is different from the first category. An eligibility of the user to receive an item recommendation in the second category can be determined based on the degree of cross-category intent. A recommended item in the second category may be determined based on the eligibility, and presented to the user together with the anchor item in the user interface.

Abstract: In some embodiments, apparatuses and methods are provided herein useful to generate personalized content. In some embodiments, a system comprising a processing resource; a machine readable medium storing instructions that, when executed, cause the processing resource to: aggregate, session data during a client session; update, periodically and during the interaction session, one or more inference indicators associated with the client in an inference cache storage, the one or more inference indicators being determined via a trained machine learning model using the session data and historical data in a historical data database; identify a trigger event based on client interactions; retrieve, in response to the trigger event, at least one inference indicator associated with the client from the inference cache storage; and generate a personalized content for display on the client device based on the at least one inference indicator retrieved from the inference cache storage.

Abstract: System and methods for dynamic distributed model loading are disclosed. In some embodiments, a disclosed method includes: storing, in a database, historical data associated with previously loaded models, receiving a model loading request associated with a first model via a user interface, identifying one or more model parameters associated with the first model, generating a score value associated with the first model based on the one or more model parameters, based on the score value, partitioning the first model into a plurality of first model segments, ranking the plurality of first model segments with a plurality of second model segments associated with a second model, and executing each of the plurality of first model segments and the plurality of second model segments based on the ranking.

Abstract: Example implementations relate to generating keywords. An item data structure including textual information is received. A first context associated with the textual information is determined and a plurality of keywords is generated using a first trained model that receives the textual information and the first context. A plurality of matching item data structures including respective textual information corresponding to a plurality of matching items associated with the item is received. A set of reference keywords is generated using a second trained model that receives the respective textual information and one or more of second contexts. A relevancy score is determined between at least one keyword and the first context using the first trained model, and an interface that includes the at least one keyword is generated.

Abstract: System and methods for controlling customer retention are disclosed. In some embodiments, a disclosed method includes: storing historical customer data and order data associated with a customer of a retailer within a database, receiving the order data within a time period, the order data including a plurality of orders, parsing the order data to determine an order status for each of the plurality of orders, identifying a negative order from the order data based on the order status, the negative order associated with the customer, generating, in real-time, appeasement data for the negative order, the appeasement data being associated based on the historical customer data, and transmitting, in real-time, the appeasement data to an electronic device having a user interface for display of an appeasement offer associated with the appeasement data to the customer.

Abstract: Systems and methods for context aware engagement are disclosed. A request for a user interface, including a user identifier, is received. A set of features associated with the user identifier are obtained and a user embedding is generated by applying an autoencoder to the set of features. A set of potential tasks associated with an enrollment portion of the user interface is obtained. A task embedding is generated for each task in the set of potential tasks. A user-task affinity is generated by comparing the user embedding to each task embedding. A ranked set of tasks is generated by ranking each task based on the user-task affinity. A set of interface elements related to the highest ranked tasks in the ranked set of tasks is generated. A user interface including interface elements is generated and transmitted to a device that requested the user interface.