Abstract: The methods and systems of the present disclosure may: receive data indicating: one or more items to be purchased by a customer from a retailer associated with an online shopping concierge platform, and one or more items purchased by the customer from the retailer; identify one or more items offered by the retailer for subsequent consideration by the customer; determine, based at least in part on one or more machine learning (ML) models, one or more values for each item representing a likelihood that the customer will purchase the item if subsequently presented with the opportunity under specified conditions; generate data describing a graphical user interface (GUI) comprising a ranked listing of at least a portion of the item(s); and communicate the data describing the GUI such that a computing device associated with the customer renders and displays the listing.

Abstract: A trained model is used to generate action recommendations by predicting different metrics related to items ordered at an online system. The online system gathers replacement data related to replacement of an item either online or at a location of a retailer when a user uses a physical receptacle in communication with the online system. The online system applies a metric prediction model trained to predict, based on the replacement data, an irreplaceability score indicative of an item's irreplaceability feature and/or a brand stickiness score indicative of an item's brand stickiness feature. The online system applies an item replacement model trained to identify, based in part on the irreplaceability score and/or the brand stickiness score, a list of candidate replacement items. A device associated with the user displays a user interface with a replacement item from the list prompting the user to include the replacement item to a cart.

Abstract: A trained model is used to generate action recommendations by predicting different metrics related to items ordered at an online system. The online system gathers replacement data related to replacement of an item either online or at a location of a retailer when a user uses a physical receptacle in communication with the online system. The online system applies a metric prediction model trained to predict, based on the replacement data, an irreplaceability score indicative of an item's irreplaceability feature and/or a brand stickiness score indicative of an item's brand stickiness feature. The online system applies an item replacement model trained to identify, based in part on the irreplaceability score and/or the brand stickiness score, a list of candidate replacement items. A device associated with the user displays a user interface with a replacement item from the list prompting the user to include the replacement item to a cart.