Determining generic items for orders on an online concierge system

Abstract

An online system provides options for selection by a user. The online system receives a query entered on a client device. The online system queries an item database to retrieve a set of items related to the query and assigns each item to a product category in a predefined taxonomy that maps items to product categories. The online system inputs each item into a prediction model trained to predict a probability that an item is available at a warehouse location. The online system determines that a first product category has low availability based on predicted probabilities for items in the first product category. Responsive to determining that a first product category has low availability, the online system generates a generic item for the first product category and sends a list of items including the generic item to the client device for display responsive to the query.

What is claimed is: 
     
         1 . A method comprising, at a computer system comprising a processor and memory:
 receiving a request to present a set of items to a user, wherein the set of items corresponds to a product category of a plurality of product categories;   generating an availability prediction for each item of the set of items by applying a prediction model to each item of the set of items, wherein the availability prediction for each item of the set of items is a prediction of the likelihood that the item is available at a warehouse location, and wherein the prediction model is a machine learning model trained to predict a probability that an item is available at a warehouse location based on the warehouse location and a plurality of characteristics associated with the item,   determining that the product category has low availability based on the generated availability predictions for the set of items;   responsive to determining that the product category has low availability, generating a generic item for the product category; and   sending a list of items to the user device for display to the user, wherein the list of items includes the generic item as one of the items.   
     
     
         2 . The method of  claim 1 , further comprising:
 storing a hierarchical taxonomy of product categories, wherein the hierarchical taxonomy maps items to product categories in the plurality of product categories.   
     
     
         3 . The method of  claim 2 , wherein the product category for the set of items is a leaf category of the hierarchical taxonomy. 
     
     
         4 . The method of  claim 1 , wherein the prediction model is a machine-learning model trained by a process comprising:
 obtaining training data that describes, for each of a plurality of training examples, an item included in a previous order, whether the item in the previous order was picked, and a plurality of characteristics associated with the item,   for each training example of the plurality of training examples, updating the prediction model by:
 inputting a warehouse location and the plurality of characteristics associated with the item into the prediction model to generate a probability indicative of whether the item is available at the warehouse location; and 
 updating the prediction model based on the generated probability and whether the item was picked. 
   
     
     
         5 . The method of  claim 1 , wherein determining that the product category has low availability comprises:
 computing a total predicted likelihood for the product category based on the generated availability predictions for the set of items; and   comparing the total predicted likelihood to a threshold value.   
     
     
         6 . The method of  claim 1 , further comprising:
 accessing a price for each item in the set of items;   computing an interior price range for the product category based on the accessed prices for the set of items; and   sending the interior price range to the user device to be displayed with the generic item.   
     
     
         7 . The method of  claim 6 , wherein computing the interior price range comprises:
 computing a low end of the interior price range by computing a first percentile value of the accessed prices; and   computing a high end of the interior price range by computing a second percentile value for the accessed prices.   
     
     
         8 . The method of  claim 6 , wherein accessing a price for each item in the set of items comprises:
 determining a most common size of the set of items for the price category; and   accessing a price associated with the most common size for each item in the set of items.   
     
     
         9 . The method of  claim 6 , further comprising:
 receiving an indication from the user device that the generic item was added to an order placed by the user;   determining a maximum price for the generic item based on the accessed prices for the set of items; and   transmitting the maximum price to a user device associated with a picker for display to the picker along with the generic item.   
     
     
         10 . The method of  claim 9 , wherein the maximum price is a high end of the interior price range. 
     
     
         11 . A non-transitory computer-readable medium storing instructions that, when executed by a processor, cause the processor to perform operations comprising:
 receiving a request to present a set of items to a user, wherein the set of items corresponds to a product category of a plurality of product categories;   generating an availability prediction for each item of the set of items by applying a prediction model to each item of the set of items, wherein the availability prediction for each item of the set of items is a prediction of the likelihood that the item is available at a warehouse location, and wherein the prediction model is a machine learning model trained to predict a probability that an item is available at a warehouse location based on the warehouse location and a plurality of characteristics associated with the item,   determining that the product category has low availability based on the generated availability predictions for the set of items;   responsive to determining that the product category has low availability, generating a generic item for the product category; and   sending a list of items to the user device for display to the user, wherein the list of items includes the generic item as one of the items.   
     
     
         12 . The computer-readable medium of  claim 11 , wherein the operations further comprise:
 storing a hierarchical taxonomy of product categories, wherein the hierarchical taxonomy maps items to product categories in the plurality of product categories.   
     
     
         13 . The computer-readable medium of  claim 12 , wherein the product category for the set of items is a leaf category of the hierarchical taxonomy. 
     
     
         14 . The computer-readable medium of  claim 11 , wherein the prediction model is a machine-learning model trained by a process comprising:
 obtaining training data that describes, for each of a plurality of training examples, an item included in a previous order, whether the item in the previous order was picked, and a plurality of characteristics associated with the item,   for each training example of the plurality of training examples, updating the prediction model by:
 inputting a warehouse location and the plurality of characteristics associated with the item into the prediction model to generate a probability indicative of whether the item is available at the warehouse location; and 
 updating the prediction model based on the generated probability and whether the item was picked. 
   
     
     
         15 . The computer-readable medium of  claim 11 , wherein determining that the product category has low availability comprises:
 computing a total predicted likelihood for the product category based on the generated availability predictions for the set of items; and   comparing the total predicted likelihood to a threshold value.   
     
     
         16 . The computer-readable medium of  claim 11 , wherein the operations further comprise:
 accessing a price for each item in the set of items;   computing an interior price range for the product category based on the accessed prices for the set of items; and   sending the interior price range to the user device to be displayed with the generic item.   
     
     
         17 . The computer-readable medium of  claim 16 , wherein computing the interior price range comprises:
 computing a low end of the interior price range by computing a first percentile value of the accessed prices; and   computing a high end of the interior price range by computing a second percentile value for the accessed prices.   
     
     
         18 . The computer-readable medium of  claim 16 , wherein accessing a price for each item in the set of items comprises:
 determining a most common size of the set of items for the price category; and   accessing a price associated with the most common size for each item in the set of items.   
     
     
         19 . The computer-readable medium of  claim 16 , wherein the operations further comprise:
 receiving an indication from the user device that the generic item was added to an order placed by the user;   determining a maximum price for the generic item based on the accessed prices for the set of items; and   transmitting the maximum price to a user device associated with a picker for display to the picker along with the generic item.   
     
     
         20 . The computer-readable medium of  claim 19 , wherein the maximum price is a high end of the interior price range.