Abstract: Techniques for dynamic result boosting are provided. Embodiments include determining one or more attributes related to a user of a software application. Embodiments include generating, using a machine learning model, an initial ordered list of items based on the one or more attributes. Embodiments include automatically modifying the initial ordered list of items, based on a boosting factor associated with a particular item in the initial ordered list of items, and based on one or more boosting constraints, to produce a modified ordered list of items in which the particular item occupies a higher position than an initial position of the particular item in the initial ordered list of items. The one or more boosting constraints may relate to the initial position of the particular item in the initial ordered list of items. Embodiments include modifying a user interface based on the modified ordered list of items.

Abstract: A method for training a machine learning model to automatically recommend substitute grocery products for a grocery product selected by a user is provided. The method includes generating a hierarchical structure defining a relationship between each of a plurality of different grocery products based, at least in part, on one or more of a plurality of different attributes associated with each of the grocery products. The method includes generating training data based on the hierarchical structure, the training data comprising, for each respective grocery product included in a subset of the plurality of different grocery products, a list of candidate substitute grocery products ranked from most substitutable to least substitutable. The method includes training the machine learning model using the training data.