Abstract: Techniques are disclosed for training a machine learning model to identify and rank entities relative to a set of requirements. The trained machine learning model may present an array of interface elements (e.g., icons) in a graphical user interface (GUI), where the interface elements represent corresponding entities. These interface elements are arranged in the GUI based on their corresponding ranks. The ranks of entities, and therefore the locations of corresponding interface elements are based, at least in part, on a degree of match between values of a subset of entity attributes and a corresponding subset of the set of requirements. The machine learning model may be further trained by receiving a user input that changes a location of a particular user interface element within the graphical user interface displaying the ranked user interface elements.

Abstract: Techniques are disclosed for training a machine learning model to identify and rank entities relative to a set of requirements. The trained machine learning model may present an array of interface elements (e.g., icons) in a graphical user interface (GUI), where the interface elements represent corresponding entities. These interface elements are arranged in the GUI based on their corresponding ranks. The ranks of entities, and therefore the locations of corresponding interface elements are based, at least in part, on a degree of match between values of a subset of entity attributes and a corresponding subset of the set of requirements. The machine learning model may be further trained by receiving a user input that changes a location of a particular user interface element within the graphical user interface displaying the ranked user interface elements.

Abstract: Techniques are disclosed for using a machine learning model to identify and present a ranked array of interface elements representing entities. The location of individual interface elements within the ranked array of interface elements is based on a level of match between entity attributes and a set of requirements established by a user. The machine learning model may be further trained by receiving a user input that changes a location of a particular user interface element within a graphical user interface displaying the ranked array. Upon receiving the user input, the trained machine learning model may update training data to include an updated match score for the particular user interface element that reflects the new location.