Search and Recommendations Archives

How DoorDash leverages LLMs for better search retrieval

At DoorDash, users commonly conduct searches using precise queries that compound multiple requirements.

Selecting the Best Image for Each Merchant Using Exploration and Machine Learning

In order to inspire DoorDash consumers to order from the platform there are few tools more powerful than a compelling image, which raises the questions: what is the best image to show each customer, and how can we build a model to determine that programmatically using each merchant’s available images?

Homepage Recommendation with Exploitation and Exploration

Building quality recommendations and personalizations requires delicately balancing what is already known about users while recommending new things that they might like.

Evolving DoorDash’s Substitution Recommendations Algorithm

When expanding from made-to-order food delivery to new product verticals like groceries, convenience, and retail, new challenges arise, including how to ensure inventory will be available to fulfill orders.

Building the Model Behind DoorDash’s Expansive Merchant Selection

An optimized merchant selection strategy has been one of the key factors that has enabled DoorDash to become an industry leader in US food delivery service.

Things Not Strings: Understanding Search Intent with Better Recall

For every growing company using an out-of-the-box search solution there comes a point when the corpus and query volume get so big that developing a system to understand user search intent is needed to consistently show relevant results.

We ran into a similar problem at DoorDash where, after we set up a basic “out-of-the-box” search engine, the team focused largely on reliability.

Personalized Cuisine Filter

The consumer shopping experience is a key focus area at DoorDash.

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.