Abstract: A computer-implemented method may include hosting an article sharing portal accessible by the one or more electronic tenant interfaces, the article sharing portal being responsive to actions performed with the one or more electronic tenant interfaces to create, modify, or delete inventory data for sharing one or more articles across a plurality of inventories. The method may include receiving user selections initiated at user platforms for articles described in the received wearable article data, including a first shared article included in an electronic inventory of a first tenant, the first shared article being placed in the electronic inventory of the first tenant by the actions performed with the one or more electronic tenant interfaces for sharing the articles, and in response to receiving the one or more user selections, initiating one or more microservices to facilitate physical shipment of the first shared article to a user.

Abstract: A computer-implemented method may include hosting an article sharing portal accessible by the one or more electronic tenant interfaces, the article sharing portal being responsive to actions performed with the one or more electronic tenant interfaces to create, modify, or delete inventory data for sharing one or more articles across a plurality of inventories. The method may include receiving user selections initiated at user platforms for articles described in the received wearable article data, including a first shared article included in an electronic inventory of a first tenant, the first shared article being placed in the electronic inventory of the first tenant by the actions performed with the one or more electronic tenant interfaces for sharing the articles, and in response to receiving the one or more user selections, initiating one or more microservices to facilitate physical shipment of the first shared article to a user.

Abstract: A computer-implemented method may include hosting an article sharing portal accessible by the one or more electronic tenant interfaces, the article sharing portal being responsive to actions performed with the one or more electronic tenant interfaces to create, modify, or delete inventory data for sharing one or more articles across a plurality of inventories. The method may include receiving user selections initiated at user platforms for articles described in the received wearable article data, including a first shared article included in an electronic inventory of a first tenant, the first shared article being placed in the electronic inventory of the first tenant by the actions performed with the one or more electronic tenant interfaces for sharing the articles, and in response to receiving the one or more user selections, initiating one or more microservices to facilitate physical shipment of the first shared article to a user.

Abstract: Disclosed are methods, systems, and non-transitory computer-readable medium for executing neural network training for dynamically predicting apparel wearability. For example, a method may include generating a training data set comprising one or more historical data attributes of previously shipped apparel, training a neural network based on the training data set to configure one or more trained models to output a metric for any pair of a unique user identifier and a unique apparel identifier, storing one or more trained model objects, collecting prediction data comprising at least one prediction pair including a unique user identifier and a unique apparel identifier, predicting one or more predictive wearability metrics indicative of propensity to wear, dynamically generating one or more match pairs, and determining a match wearability metric for each of the one or more match pairs based on the predicted one or more predictive wearability metrics.

Abstract: Disclosed are methods, systems, and non-transitory computer-readable medium for assigning wearable items in a subscription electronics transactions platform. For example, a method may include: generating a grid based on information regarding historically shipped wearable items, wherein the grid comprises at least a first cell and a second cell; determining an average percentage indicating how many wearable items have been used and an average predictive wearability metric for wearable items indicative of a propensity of a user to use the wearable items per number of wearable items shipped for each cell; generating a mapping configured to convert a predictive wearability metric to a squashed predictive wearability metric; and converting a first predictive wearability metric to a first squashed wearability metric based on the generated mapping.

Abstract: Disclosed are methods, systems, and non-transitory computer-readable medium for executing neural network training for dynamically predicting apparel wearability. For example, a method may include generating a training data set comprising one or more historical data attributes of previously shipped apparel, training a neural network based on the training data set to configure one or more trained models to output a metric for any pair of a unique user identifier and a unique apparel identifier, storing one or more trained model objects, collecting prediction data comprising at least one prediction pair including a unique user identifier and a unique apparel identifier, predicting one or more predictive wearability metrics indicative of propensity to wear, dynamically generating one or more match pairs, and determining a match wearability metric for each of the one or more match pairs based on the predicted one or more predictive wearability metrics.

Abstract: Disclosed are methods, systems, and non-transitory computer-readable medium for assigning wearable items in a subscription electronics transactions platform. For example, a method may include: generating a grid based on information regarding historically shipped wearable items, wherein the grid comprises at least a first cell and a second cell; determining an average percentage indicating how many wearable items have been used and an average predictive wearability metric for wearable items indicative of a propensity of a user to use the wearable items per number of wearable items shipped for each cell; generating a mapping configured to convert a predictive wearability metric to a squashed predictive wearability metric; and converting a first predictive wearability metric to a first squashed wearability metric based on the generated mapping.

Abstract: Disclosed are methods, systems, and non-transitory computer-readable medium for executing neural network training for dynamically predicting apparel wearability. For example, a method may include generating a training data set comprising one or more historical data attributes of previously shipped apparel, training a neural network based on the training data set to configure one or more trained models to output a metric for any pair of a unique user identifier and a unique apparel identifier, storing one or more trained model objects, collecting prediction data comprising at least one prediction pair including a unique user identifier and a unique apparel identifier, predicting one or more predictive wearability metrics indicative of propensity to wear, dynamically generating one or more match pairs, and determining a match wearability metric for each of the one or more match pairs based on the predicted one or more predictive wearability metrics.