Menu Personalization

Abstract

Provided are systems, methods, and computer-program products for generating a personalized item list. In various examples, a server computer on a network can receive a request that includes a user identifier. The computer can use the user identifier to look up a data model associated with the user identifier. The computer can further determine a geolocation, and use the geolocation to determine a list of items associated with an eatery at or near the geolocation. The computer can input the item list into the data model, for the data model to output a probability for each item, the probability indicating a likelihood that the user will select the item. The probabilities can be used to generate a personalized item list, which can be output onto the network for receipt by a computing device.

1 - 20 . (canceled) 
     
     
         21 . A computer-implemented method comprising:
 receiving, over a network, data corresponding to a request for a personalized item list, wherein the data comprises a user identifier associated with a user and data associated with a computing device;   obtaining, from a data store based at least in part on the user identifier, a particular data model associated with the user identifier, the data store storing a plurality of data models associated with a plurality of user identifiers, each data model being trained based on data associated with a respective user;   determining a geolocation associated with the user identifier;   determining, using the geolocation, a plurality of menu items associated with an eatery;   inputting the plurality of menu items associated with the eatery into the data model;   generating, using the data model, a selection probability value for each menu item in the plurality of menu items;   determining a subset of menu items from the plurality of menu items based at least in part on the selection probability value associated with each menu item; and   providing, to the computing device, output data indicative of the subset of menu items, wherein the output data instructs the computing, device to display the subset of menu items.   
     
     
         22 . The computer-implemented method of  claim 21 , wherein the menu items associated with the eatery comprises a listing of edible ingredients of each menu item. 
     
     
         23 . The computer-implemented method of  claim 22 , wherein the generating the selection probability value for each menu item in the plurality of mcmi items is based on at least one of the listing of edible ingredients of each menu item or an attribute associated with the menu item. 
     
     
         24 . The computer-implemented method of  claim 21 , wherein the determining the subset of menu items from the plurality of menu items based at least in part on the selection probability value associated with each menu item comprises:
 sorting the subset of menu items according to the selection probability value associated with each menu item.   
     
     
         25 . The computer-implemented method of  claim 24 , wherein the determining the subset of menu items from the plurality of menu items based at least in part on the selection probability value associated with each menu item comprises:
 filtering the sorted subset of menu items to remove items that at least one of (i) do not meet one or more dietary restrictions associated with the user, (ii) items that are marked as disliked by the user, or (iii) items that are unavailable due to time constraints.   
     
     
         26 . The computer-implemented method of  claim 21 , comprising:
 associating numerical scores with each item in the subset of menu items; and   resorting the subset of menu items according to the numerical scores.   
     
     
         27 . The computer-implemented method of  claim 26 , the comprising:
 determining a current time; and   modifying the numerical scores according to the current time.   
     
     
         28 . The computer-implemented method of  claim 26 , the comprising:
 determining a recent item selection associated with the user identifier, wherein the recent item selection is determined from within a pre-determined time period preceding receipt of the data corresponding to the request for the personalized item list; and   modifying the numerical scores according to the recent item selection.   
     
     
         29 . A computing system, comprising:
 one or more processors; and   one or more non-transitory, computer-readable media storing instructions that are executable by the one or more processors to cause the computing system to perform operations, the operations comprising:
 receiving, over a network, data corresponding to a request for a personalized item list, wherein the data comprises a user identifier associated with a user and data associated with a computing device; 
 obtaining, from a data store based at least in part on the user identifier, a particular data model associated with the user identifier, the data store storing a plurality of data models associated with a plurality of user identifiers, each data model being trained based on data associated with a respective user; 
 determining a geolocation associated with the user identifier; 
 determining, using the geolocation, a plurality of menu items associated with an eatery; 
 inputting the plurality of menu items associated with the eatery into the data model; 
 generating, using the data model, a selection probability value for each menu item in the plurality of menu items; 
 determining a subset of menu items from the plurality of menu items based at least in part on the selection probability value associated with each menu item; and 
 providing, to the computing device, output data indicative of the subset of menu items, wherein the output data instructs the computing device to display the subset of menu items. 
   
     
     
         30 . The computing system of  claim 29 , wherein determining the geolocation associated with the user identifier and generating the personalized item list occurs at a point prior to receiving the data corresponding to the request for the personalized item list, such that receiving the data corresponding to the request for the personalized item list results in the providing, to the computing device, the output data indicative of the subset of menu items. 
     
     
         31 . The computing system of  claim 29 , wherein the subset of menu items is associated with a particular eatery at the geolocation. 
     
     
         32 . The computing system of  claim 29 , wherein each of the plurality of data models is trained using a machine learning method. 
     
     
         33 . The computing system of  claim 29 , the operations comprising:
 receiving data indicative of user input corresponding to an item selection; and   adding the item selection to the particular data model.   
     
     
         34 . The computing system of  claim 29 , the operations comprising:
 receiving data indicative of user input corresponding to a value associated with an item from the personalized item list, wherein the value is a positive value or a negative value; and   adding the value to the particular data model.   
     
     
         35 . The computing system of  claim 29 , wherein the menu items associated with the eatery comprises a listing of edible ingredients of each menu item. 
     
     
         36 . The computing system of  claim 35 , wherein the generating the selection probability value for each menu item in the plurality of menu items is based on at least one of the listing of edible ingredients of each menu item or an attribute associated with the menu item. 
     
     
         37 . The computing system of  claim 29 , wherein the determining the subset of menu items from the plurality of menu items based at least in part on the selection probability value associated with each menu item comprises:
 sorting the subset of menu items according to the selection probability value associated with each menu item.   
     
     
         38 . One or more non-transitory computer readable media storing instructions that are executable by one or more processors to perform operations comprising:
 receiving, over a network, data corresponding to a request for a personalized item list, wherein the data comprises a user identifier associated with a user and data associated with a computing device;   obtaining, from a data store based at least in part on the user identifier, a particular data model associated with the user identifier, the data store storing a plurality of data models associated with a plurality of user identifiers, each data model being trained based on data associated with a respective user;   determining a geolocation associated with the user identifier;   determining, using the geolocation, a plurality of menu items associated with an eatery;   inputting the plurality of menu items associated with the eatery into the data model;   generating, using the data model, a selection probability value for each menu item in the plurality of menu items;   determining a subset of menu items from the plurality of menu items based at least in part on the selection probability value associated with each menu item; and   providing, to the computing device, output data indicative of the subset of menu items, wherein the output data instructs the computing device to display the subset of menu items.   
     
     
         39 . The one or more non-transitory computer readable media of  claim 38 , the determining the subset of menu items from the plurality of menu items based at least in part on the selection probability value associated with each menu item comprises:
 sorting the subset of menu items according to the selection probability value associated with each menu item.   
     
     
         40 . The one or more non-transitory computer readable media of  claim 38 , wherein the generating the selection probability value for each menu item in the plurality of menu items is based on at least one of a listing of edible ingredients of each menu item or an attribute associated with the menu item.