Maplebear Inc.

Levi Boxell

Tilman Drerup

Vinesh Reddy Gudla

Jonathan Gu

Raochuan Fan

Yixi Ouyang

Bo Xiao

Matias Cersosimo

Jennifer Wiersema

Rustin Partow

Oleksii Stepanian

Michael Kurish

Ying Li

An online system displays items to a user in search results based on appeasement scores for the items, adjusted according to how specific the search query is. The online system receives a search query from a user of an online system. The online system computes a query specificity score, a measure of the specificity of the search query. The online system accesses candidate items from a database that potentially match the search query. For each candidate item, the online system may compute or predict an appeasement score. The online system adjusts the appeasement score based on the query specificity score such that a more specific query weights the appeasement score lower than a less specific query. The online system may then compute a ranking score based on the adjusted appeasement score and display the candidate items to the user based on their ranking scores.

Ranking search results based on appeasement signals and query specificity

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

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Brownsburg, IN, United States of America

Levi Boxell