Determining generic items for orders on an online concierge system

A non-transitory computer-readable medium storing instructions that, when executed by a processor, cause the processor to:

• receive a query sent from a user device to an online concierge system, wherein the query comprising a string of text entered by a user to the user device for searching the online concierge system;
• identify a set of items matching the query, wherein each item of the set of items corresponds to a product category of a plurality of product categories;
• generate 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 third-party warehouse location, and wherein the prediction model is a machine learning model trained to predict a probability that an item is available at a third-party warehouse location based on the third-party warehouse location and a plurality of characteristics associated with the item, wherein training the machine learning model comprises:
• obtaining a plurality of training examples, wherein each of the plurality of training examples comprises training data describing an availability of an item included in a previous order,
• for each training example of the plurality of training examples, updating the prediction model by:
• inputting the training data for the training example into the prediction model, wherein the prediction model generates a probability indicative of whether the item is available at a third-party warehouse location;
• computing a score for the training example based on the generated probability and the availability in the training data of the training example;
• and
• updating the prediction model based on the computed score, and
• repeating the updating until a confidence score associated with the probability that an item is available is above a threshold;
• determine 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, generate a generic item for the product category;
• and
• send 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.

A system comprising:

• a processor;
• and
• a non-transitory computer-readable medium storing instructions that, when executed by the processor, cause the processor to:
• receive a query sent from a user device to an online concierge system, wherein the query comprising a string of text entered by a user to the user device for searching the online concierge system;
• identify a set of items matching the query, wherein each item of the set of items corresponds to a product category of a plurality of product categories;
• generate 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 third-party warehouse location, and wherein the prediction model is a machine learning model trained to predict a probability that an item is available at a third-party warehouse location based on the third-party warehouse location and a plurality of characteristics associated with the item, wherein training the machine learning model comprises:
• obtaining a plurality of training examples, wherein each of the plurality of training examples comprises training data describing an availability of an item included in a previous order,
• for each training example of the plurality of training examples, updating the prediction model by:
• inputting the training data for the training example into the prediction model, wherein the prediction model generates a probability indicative of whether the item is available at a third-party warehouse location;
• computing a score for the training example based on the generated probability and the availability in the training data of the training example;
• and
• updating the prediction model based on the computed score, and
• repeating the updating until a confidence score associated with the probability that an item is available is above a threshold;
• determine 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, generate a generic item for the product category;
• and
• send 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.