Abstract: The present disclosure provides methods and systems for providing recommendations of items to a user that are based on the user's preferences of attributes of those items. The system collects user session data and identifies attributes of items that the user may have interacted with in a previous shopping session and to assign user interest scores to the attributes based on the type user interaction with the item (e.g., selected, added to the user's shopping cart, or purchased). A decay factory may be applied to each user interest score to adjust the score over time. When a request is received for an item recommendation for the user, the system may determine user attribute preference (UAP) scores for a group of items based on the user interest scores and provide a UAP-based recommendation in a response based on the UAP scores.

Abstract: Methods and systems for optimizing a product assortment, and managing product fulfillment, are disclosed. One method includes utilizing a model trained on item data to identify item substitution pairs within an item category of an item assortment, the item substitution pairs being identified as having a substitutability score above a predetermined threshold. The method further includes applying an assortment optimization model to generate an assortment recommendation for the item category at an identified retail location. A request for an item not included in the assortment recommendation may result in suggestion of an identified substitutable item from among the item substitution pairs.