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Machine Learning

Machine Learning

Personalized Store Feed with Vector Embeddings

Customers come to DoorDash to discover and order from a vast selection of their favorite stores, so it is important to be able to surface what is most relevant to them. In a previous article, Powering Search & Recommendations at DoorDash, we discussed how we built our initial personalized search and discovery experience to surface the ...

Mitchell Koch Aamir Manasawala
Machine Learning

Switchback Tests and Randomized Experimentation Under Network Effects at DoorDash

To A/B or not to A/B, that is the question Overview On the Dispatch team at DoorDash, we use simulation, empirical observation, and experimentation to make progress towards our goals; however, given the systemic nature of many of our products, simple A/B tests are often ineffective due to network effects. To be able to experiment ...

David Kastelman Raghav Ramesh
Machine Learning

Powering Search & Recommendations at DoorDash

Customers across North America come to DoorDash to discover and order from a vast selection of their favorite stores. Our mission is to surface the best stores for our consumers based on their personal preferences. However, the notion of “best stores” for a consumer varies widely based on their diet, taste, budget, and other preferences. ...

Aamir Manasawala Mitchell Koch
Machine Learning

How To Get from Salad to Sushi in 3 Moves

At DoorDash, we want to make it as easy as possible for people to discover and order from great restaurants in their neighborhoods. As part of that goal, one fascinating problem we’re tackling is how to create a personalized experience for each user on the DoorDash platform by surfacing recommendations for restaurants and other merchants ...

Mitchell Koch

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