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 predicting relevant items to be presented to a user in an online environment are described. The methods and systems described herein generate models based on previous item selections to determine an overall time series model for predicting a relevant time of next item selection as well as items most likely to be selected at that time. Complementary items can be presented to the user alongside the selection of most relevant items.

Abstract: Methods and systems for generating and presenting item recommendations to a user at a retail website is disclosed. One method includes generating a set of item recommendations based on a selected item from a retail website. The method also includes identifying one or more facets in the set of item recommendations, and, for each of the one or more facets, identifying a subset of items from the set of item recommendations affiliated with the facet. The method also includes generating a user interface including the selected item and a recommendation region displaying at least some of the subset of items affiliated with the facet.

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 generating and presenting item recommendations to a user at a retail website is disclosed. One method includes generating a set of item recommendations based on a selected item from a retail website. The method also includes identifying one or more facets in the set of item recommendations, and, for each of the one or more facets, identifying a subset of items from the set of item recommendations affiliated with the facet. The method also includes generating a user interface including the selected item and a recommendation region displaying at least some of the subset of items affiliated with the facet.

Abstract: Methods and systems for predicting relevant items to be presented to a user in an online environment are described. The methods and systems described herein generate models based on previous item selections to determine an overall time series model for predicting a relevant time of next item selection as well as items most likely to be selected at that time. Complementary items can be presented to the user alongside the selection of most relevant items.