Abstract: A system for targeting advertising comprises a processor configured to compute a similarity score between a plurality of customers in a seed set and each of a pool of in-store purchasers having no history of online purchases from the online store of an entity. Each user in the seed set has a history of online purchases from the online store of the entity and a history of in-store purchases from stores of the entity. The processor is configured for selecting a subset of the pool of in-store purchasers having similarity scores above a predetermined threshold. The processor is configured for initiating rendering of advertisements for the online store to the subset of the pool of in-store purchasers.

Abstract: Systems and methods including one or more processors and one or more non-transitory storage devices storing computing instructions configured to run on the one or more processors and perform receiving a request for a machine learned prediction for an item; determining that a number of entries in item user interaction data of the item is below a predetermined number-of-entries threshold; incorporating category interaction data of a hierarchical category of the item from a predetermined time period into the item user interaction data of the item; determining an initial machine learned prediction for the item using at least the category interaction data, as incorporated into the item user interaction data; and facilitating altering a graphical user interface (GUI) of an electronic device using the initial machine learned prediction for the item. Other embodiments are disclosed herein.

Abstract: Systems and methods including one or more processors and one or more non-transitory storage devices storing computing instructions configured to run on the one or more processors and perform receiving a request for a machine learned prediction for an item; determining that a number of entries in item user interaction data of the item is below a predetermined number-of-entries threshold; incorporating category interaction data of a hierarchical category of the item from a predetermined time period into the item user interaction data of the item; determining an initial machine learned prediction for the item using at least the category interaction data, as incorporated into the item user interaction data; and facilitating altering a graphical user interface (GUI) of an electronic device using the initial machine learned prediction for the item. Other embodiments are disclosed herein.

Abstract: A system for targeting advertising comprises a processor configured to compute a similarity score between a plurality of customers in a seed set and each of a pool of in-store purchasers having no history of online purchases from the online store of an entity. Each user in the seed set has a history of online purchases from the online store of the entity and a history of in-store purchases from stores of the entity. The processor is configured for selecting a subset of the pool of in-store purchasers having similarity scores above a predetermined threshold. The processor is configured for initiating rendering of advertisements for the online store to the subset of the pool of in-store purchasers.