Abstract: An item recommendation control platform is disclosed. The item recommendation control platform presents an administrative user one or more user interfaces at which recommendation rules may be defined. Each of the recommendation rules may be associated with one or more nodes within a retail website, such as an item detail page or an item category page. The recommendation rules may be selected, defined, and prioritized, such that one or more item recommendation rules may be reflected within a given item recommendation presented on a retail website. The item recommendation rules can include one or more filters, the filters controlling which items may be recommended to a given user based, for example, on item availability or shipping availability.

Abstract: Methods and systems for forecasting demand for items across multiple channels are disclosed. In some implementations, multi-channel demand forecasting may be performed on a per-item, per-location basis, by selectively generating item-location forecasts for each item and location within a supply chain for each channel, or disaggregating a chain level forecast on a per-item basis to each location. Particular selection of an appropriate model, and selective training of models, allows for efficient computation of such forecasts across a large supply chain with thousands of locations and hundreds of thousands, or millions, of items for which forecasts are generated.

Abstract: An item recommendation control platform is disclosed. The item recommendation control platform presents an administrative user one or more user interfaces at which recommendation rules may be defined. Each of the recommendation rules may be associated with one or more nodes within a retail website, such as an item detail page or an item category page. The recommendation rules may be selected, defined, and prioritized, such that one or more item recommendation rules may be reflected within a given item recommendation presented on a retail website. The item recommendation rules can include one or more filters, the filters controlling which items may be recommended to a given user based, for example, on item availability or shipping availability.

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.