Maplebear Inc.

Jonathan Gu

Sharath Rao Karikurve

Peng Qi

Jeffrey Bernard Arnold

Tilman Drerup

Levi Boxell

Rob Donnelly

David Pal

Yixi Ouyang

Ying Li

Bo Xiao

Matias Cersosimo

Jennifer Wiersema

Sumit Garg

Rustin Partow

Bill Lundberg

Oleksii Stepanian

An online system uses an offline iterative clustering process to evaluate the performance of a set of content selection frameworks. To perform an iteration of the iterative clustering process, an online system clusters the testing example data into a set of clusters. An online system computes a set of framework scores for each of the generated clusters. An online system computes an improvement score for each cluster based on the performance scores of the clusters. To determine whether to perform another iteration, an online system computes an aggregated improvement score based on the improvement scores of the clusters. If an online system determines that the aggregated improvement score does not meet the threshold, an online system performs another iteration of the process above. When an online system finishes the iterative process, an online system outputs the improvement scores of the most-recent iteration.

Counterfactual policy evaluation of model performance

An online concierge system includes sponsored content items in an interface including different slots for displaying content items. A sponsored content item may be displayed in a single slot or in multiple adjacent slots. The online concierge system determines a content score for various sponsored content items indicating a likelihood of a user interacting with a sponsored content item and a position bias for slots in the interface indicating a likelihood of the user interacting with a slot independent of content in the slot. Position biases are different dependent on a number of slots in which a content item is displayed. The online concierge system generates a graph identifying potential placements of sponsored content items in slots by selecting content items in an order according to their content scores. Sponsored content items are positioned in slots according to a path through the graph that has the highest overall expected value.

Method, computer program product, and system for accounting for variable dimensions of content items when positioning content items in a user interface having slots for displaying content items

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San Francisco, CA, United States of America

Jonathan Gu