Abstract: In an example method, a system receives a first data set representing a plurality of first purchases of a plurality of first products by a plurality of first users, and trains a generative transformer model including one or more computerized attention mechanisms using the first data set as an input. Further, the system receives a second data set representing one or more second products selected by a second user for purchase, and provides the second data set to the generative transformer model. The system outputs a third data set generated by the generative transformer model based on the second data set, where the third data set represents a prediction of one or more third products for purchase by the second user, and stores the third data set using one or more computer storage devices.

Abstract: Systems and methods for identifying item substitutions. History information can be collected. The history information can be transformed into a matrix of observed substitutions. A neural network can be trained on the matrix of observed substitutions to generate item embeddings. A substitution similarity between the item and another item based on the item embeddings can be identified.

Abstract: Systems and methods for identifying item substitutions. History information can be collected. The history information can be transformed into a matrix of observed substitutions. A neural network can be trained on the matrix of observed substitutions to generate item embeddings. A substitution similarity between the item and another item based on the item embeddings can be identified.

Abstract: Systems and methods are disclosed for displaying information related to items in a store. Stock keeping unit (SKU) information for items in the store and combination information indicating which SKUs were sold together can be determined.

Abstract: Systems and methods for identifying item substitutions. History information can be collected. The history information can be transformed into a matrix of observed substitutions. A neural network can be trained on the matrix of observed substitutions to generate item embeddings. A substitution similarity between the item and another item based on the item embeddings can be identified.

Abstract: Systems and methods for identifying item substitutions. History information can be collected. The history information can include one or more episodes from one or more customers. Each episode can include one or more items. The history information can be transformed into a matrix of observed substitutions. A neural network can be trained on the matrix of observed substitutions to generate item embeddings. Input including an item can be received. A substitution similarity between the item and another item based on the item embeddings can be identified.

Abstract: Systems and methods for identifying item substitutions. History information can be collected. The history information can include one or more episodes from one or more customers. Each episode can include one or more items. The history information can be transformed into a matrix of observed substitutions. A neural network can be trained on the matrix of observed substitutions to generate item embeddings. Input including an item can be received. A substitution similarity between the item and another item based on the item embeddings can be identified. The item embeddings can be clustered to generate need states.

Abstract: Systems and methods are disclosed for displaying information related to items in a store. Stock keeping unit (SKU) information for items in the store and combination information indicating which SKUs were sold together can be accepted. Dimension information and structure information for a neural network can be defined. The neural network can be trained using the combination information indicating which SKUs were sold together in a same combination to calculate weight values and bias values for the neural network.

Abstract: Systems and methods are disclosed for displaying information related to items in a store. Stock keeping unit (SKU) information for items in the store and combination information indicating which SKUs were sold together can be determined.

Abstract: Systems and methods are disclosed for displaying information related to items in a store. Stock keeping unit (SKU) information for items in the store and combination information indicating which SKUs were sold together can be accepted. Dimension information and structure information for a neural network can be defined. The neural network can be trained using the combination information indicating which SKUs were sold together in a same combination to calculate weight values and bias values for the neural network.