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AI, Engineering

Food Discovery with Uber Eats: Recommending for the Marketplace

September 10, 2018 / Global
Featured image for Food Discovery with Uber Eats: Recommending for the Marketplace
Figure 1: This timeline illustrates our journey and the changes involved in improving our system.
Figure 2 : All parties in Uber Eats’ three-sided marketplace contribute to its overall health.
(Image credits: Restaurant-Partner: gst/Shutterstock.com, Delivery Partner: Kikuchi/Shutterstock.com, Eater: tigatelu/Depositphotos)
Figure 3: Ranking only by relevance can lead to a lack of variety, such as this list of ramen restaurants.
Figure 4: A new restaurant’s UCB score decreases over time as its impressions increase.
Figure 5: There is a steep trade-off between conversion rate and marketplace fairness.
Figure 6: Our two-layer solution combines machine learning with multi-objective optimization.
Yuyan Wang

Yuyan Wang

Yuyan Wang is a senior data scientist at Uber, currently focused on developing personalized ranking and recommendation algorithms within the Uber Eats app. She holds a Ph.D. in Statistics from Princeton University, and a bachelor’s degree from Special Class for Gifted Young at University of Science and Technology of China.

Yuanchi Ning

Yuanchi Ning

Yuanchi Ning is a senior engineer at Uber, working on optimizing personalization, ranking and recommendation challenges on the Uber Eats app.

Isaac Liu

Isaac Liu

Isaac Liu was an engineer at Uber focused on search and recommendations within the Uber Eats app. He holds a Ph.D. in Electrical Engineering and Computer Science from UC Berkeley.

Xian Xing Zhang

Xian Xing Zhang

Xian Xing is a data science manager on the UberEverything team.

Posted by Yuyan Wang, Yuanchi Ning, Isaac Liu, Xian Xing Zhang

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