Abstract: In a “buy online, pick up in-store” service, customers place orders for items that are retrieved from store inventory and packaged for easy pick-up by the customer. Since these services typically fulfill orders from current store inventory, some items purchased by users are unavailable at the time of order fulfillment. In these circumstances, a recommendation system identifies a recommended replacement item using a trained model. The trained model includes a hierarchy of multiple sub-models, where each sub-model is configured to receive a different set of features of items as input and to generate, as output, a candidate recommended replacement item. A recommended replacement item is selected from the candidate recommendations generated by the multiple sub-models and sent for display to a user. The recommendation system receives user feedback regarding the recommended replacement item and selectively retrains one or more sub-models of the multiple sub-models based on the user feedback.

Abstract: A system determines an assortment of a plurality of related products for retail placement and includes an assortment selection tool for selecting one or more of the plurality of products as a function of product data in order to define a selected product assortment, a database of additional product data and an allocation tool for adjusting the selected product assortment as a function of the additional product data in the database to define an allocated product assortment for retail placement. A virtual environment interface permits marketers, retailers and/or consumers evaluate the product assortment in a virtual environment as a function of the additional product data in the database to define an allocated product assortment for retail placement. A business method determines an assortment of a plurality of related products for retail placement. Another business method evaluates the retail placement of related products.