Maplebear Inc.

Trace Levinson

Ganesh Krishnan

Aman Jain

Ji Chen

Soren Zeliger

Jian Wang

Xiangyu Wang

Nicholas Sturm

Zhaoyu Kou

Sonali Deepak Chhabria

An online system computes an incremental cost prediction for each of a set of user-treatment pairs to select a set of treatments to apply to users to satisfy a predicted interaction gap. The online system generates a set of candidate user-treatment pairs that each include user data for a user of the online system and treatment data for a treatment of a set of treatments. The online system computes an incremental interaction prediction and a treatment cost prediction for each of the candidate user-treatment pairs by applying an incremental interaction model to the user data and the treatment data in each user-treatment pair. The online system computes incremental cost predictions for each of the user-treatment pairs based on the computed incremental interaction predictions and treatment cost predictions and selects which users to apply treatments to and which treatments to apply to those users based on the incremental cost predictions.

Incremental cost prediction for user treatment selection

An online concierge system includes a marketplace automation engine for setting various control parameters affecting marketplace operation. The marketplace automation engine applies a hyperparameter learning model to the marketplace state data to predict a set of hyperparameters affecting a set of respective parameterized control decision models. The hyperparameter learning model is trained on historical marketplace state data and a configured outcome objective for the online concierge system. The marketplace automation engine independently applies the set of parameterized control decision models to the marketplace state data using the hyperparameters to generate a respective set of control parameters affecting marketplace operation of the online concierge system. The marketplace automation engine applies the respective set of control parameters to operation of the online concierge system.

Automation engine using a hyperparameter learning model to optimize sub-systems of an online system

An online concierge system assigns shoppers to fulfill orders from users. To allocate shoppers, the online concierge system predicts future supply and demand for the shoppers' services for different time windows. To forecast a supply of shoppers, the online concierge system trains a machine learning model that estimates future supply based on access to a shopper mobile application through which the shoppers obtain new assignments by shoppers. The online concierge system also forecasts future orders. The online concierge system estimates a supply gap in a future time period by selecting a target time to accept for shoppers to accept orders and determining a corresponding ratio of number of shoppers and number of orders. The online concierge system may adjust a number of shoppers allocated to the future time period to achieve the determined ratio number of shoppers and number of orders.

Predicting shopper supply during a time interval based on interactions by shoppers with a shopper assignment application during earlier time intervals

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San Francisco, CA, United States of America

Trace Levinson