The present disclosure provides systems and methods that use machine learning to improve whole-structure relevance of hierarchical informational displays. In particular, the present disclosure provides systems and methods that employ a supervised, discriminative machine learning approach to jointly optimize the ranking of items and their display attributes. One example system includes a machine-learned display selection model that has been trained to jointly select a plurality of items and one or more attributes for each item for inclusion in an informational display. For example, the machine-learned display selection model can optimize a nested submodular objective function to jointly select the items and attributes.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computing system to select information for inclusion in informational displays, the computing system comprising: a machine-learned display selection model that has been trained to simultaneously and jointly select a plurality of items and one or more attributes for each item for inclusion in an informational display; one or more processors; and one or more non-transitory computer-readable media that collectively store instructions that, when executed by the one or more processors, cause the computing system to: obtain a plurality of candidate items that are candidates for inclusion in the informational display, wherein a plurality of candidate attributes are associated with each of the plurality of candidate items; determine a plurality of features for each of the plurality of candidate items or the candidate attributes for each candidate item; input at least a portion of the plurality of features, the plurality of candidate items, and the plurality of candidate attributes for each item into the machine-learned display selection model; and receive, as an output of the machine-learned display selection model, a single joint selection that specifies both the plurality of items and the one or more attributes for each item for inclusion in the informational display. 2. The computing system of claim 1, wherein the machine-learned display selection model is operable to optimize a nested submodular objective function to simultaneously and jointly select the plurality of items and the one or more attributes for each item. 3. The computing system of claim 1, wherein at least one of the plurality of features for at least one of the plurality of candidate items comprises a similarity feature that is descriptive of a degree of similarity between such candidate item and at least one other candidate item. 4. The computing system of claim 1, wherein at least one of the plurality of features for at least one of the plurality of candidate items comprises a similarity feature that is descriptive of a degree of similarity between one of the candidate attributes associated with such candidate item and one of the candidate attributes associated with at least one other candidate item. 5. The computing system of claim 1, wherein to determine at least one of the plurality of features for at least one of the plurality of candidate items, the computing system: determines a first neural network embedding for a first candidate item or a first candidate attribute of the first candidate item; determines a second neural network embedding for a second candidate item or a second candidate attribute of the second candidate item; and determines a difference between the first neural network embedding and the second neural network embedding, wherein the difference serves as one of the features for one or more of the first candidate item, the first candidate attribute, the second candidate item, and the second candidate attribute. 6. The computing system of claim 1, wherein the machine-learned display selection model comprises one or more structured support vector machines. 7. The computing system of claim 1, wherein the machine-learned display selection model implements a nested greedy algorithm to optimize an objective function. 8. The computing system of claim 1, wherein the plurality of items comprise a plurality of medical conditions that are responsive to a search query entered by a user, wherein the one or more attributes for each item comprise one or more symptoms for each medical condition, and wherein the informational display is included in a search results page. 9. The computing system of claim 1, wherein the single joint selection further specifies both an item ordering for the plurality of items and an attribute ordering for the one or more attributes for each item. 10. A computer-implemented method to select information for display, the method comprising identifying, by the one or more computing devices, a plurality of candidate items for potential inclusion in an informational display, wherein each candidate item has a plurality of candidate attributes associated therewith; determining, by the one or more computing devices, one or more features for each of the plurality of candidate items and for each of the plurality of candidate attributes associated with each item; inputting, by the one or more computing devices, at least a portion of the plurality of candidate items, the plurality of candidate attributes, and their associated features into a machine-learned display selection model, wherein the machine-learned display selection model is operable to optimize a nested submodular objective function to provide a display structure for use in the informational display; and receiving, by the one or more computing devices, the display structure from the machine-learned display selection model, wherein the display structure specifies a plurality of selected items selected from the plurality of candidate items, and wherein the display structure further specifies, for each selected item, one or more selected attributes selected from the plurality of candidate attributes associated with such selected item. 11. The computer-implemented method of claim 10, wherein the nested submodular objective function models one or more first dependencies between the plurality of selected items and also models one or more second dependencies between the selected attributes for two or more of the plurality of selected items. 12. The computer-implemented method of claim 10, wherein the machine-learned display selection model comprises a machine-learned structured support vector machine. 13. The computer-implemented method of claim 10, wherein the machine-learned display selection model implements a nested greedy algorithm to optimize the nested submodular objective function. 14. The computer-implemented method of claim 10, wherein the display structure ranks the plurality of selected items, and wherein the display structure further ranks the one or more selected attributes for each selected item. 15. The computer-implemented method of claim 10, further comprising: prior to said inputting, training, by the one or more computing devices, the machine-learned display selection model based at least in part on one or more preferred display structures that were manually created based on human feedback, wherein training, by the one or more computing devices, the machine-learned display selection model comprises updating, by the one or more computing devices, one or more weights or parameters included in the nested submodular objective function. 16. The computer-implemented method of claim 15, wherein training, by the one or more computing devices, the machine-learned display selection model based at least in part on the one or more preferred display structures comprises training, by the one or more computing devices, the machine-learned display selection model based at least in part on a loss function that describes a number of slots correctly filled by a predicted display structure relative to one of the preferred display structures. 17. The computer-implemented method of claim 10, further comprising: providing, by the one or more computing devices, the informational display for display to a user, wherein the informational display comprises a carousel that has the display structure output by the machine-learned display selection model. 18. One or more non-transitory computer-readable media that collectively store: a machine-learned display selection model that is operable to optimize a nested submodular objective function to output a display structure that specifies both a plurality of items and one or more attributes for each item; instructions that, when executed by one or more processors, cause a computing system to: obtain a plurality of candidate items that are responsive to a search query entered by a user, wherein a plurality of candidate attributes are associated with each of the plurality of candidate items; determine a plurality of features for each of the plurality of candidate items or the candidate attributes for each candidate item; input at least a portion of the plurality of features, the plurality of candidate items, and the plurality of candidate attributes for each item into the machine-learned display selection model; and receive, as an output of the machine-learned display selection model, the display structure that specifies both the plurality of items and one or more attributes for each item for inclusion in a search results page. 19. The one or more non-transitory computer-readable media of claim 18, wherein the machine-learned display selection model comprises one or more structured support vector machines that implement a nested greedy algorithm to optimize the nested submodular objective function. 20. The one or more non-transitory computer-readable media of claim 18, wherein the nested submodular objective function models one or more dependencies between attributes for two or more items included in the display structure.