Recommendation systems provide highly personalized results, but building hyperpersonalized experiences remains challenging because of the bottlenecks created by content generation and presentation.
Category Archives: Experimentation and Testing
Supercharging DoorDash logistics through causal ML and joint optimization
DoorDash’s delivery drivers — called Dashers — may be offered incentives such as peak pay (extra money) to improve supply during particularly busy times, in specific areas.
LLM-as-a-Judge: Evaluating natural language search
Traditional food delivery search matches keywords such as “pizza,” “sushi,” or restaurant name.
Using LLMs to build content embeddings for search and recommendations
Header Image Description: Example of semantic meaning beyond engagements
A persistent bottleneck has constrained search and recommendation functions at DoorDash for years — the caliber of content embedding depends on data quality, while personalization depends on embedding quality.
Using LLMs to infer grocery preferences from DoorDash restaurant orders
Consumers enjoy DoorDash deliveries from a variety of merchants, ranging from restaurants to pet stores.
Building an anomaly detection platform at DoorDash to catch fraud trends early
Fraud doesn’t always kick the door down.
Advancing Menu Content with AI: How DoorDash uses AI to generate menu descriptions
Our mission at DoorDash is to empower local businesses of all sizes to thrive and grow in the digital age.
How DoorDash leverages LLMs to evaluate search result pages
At DoorDash, delivering relevant and high-quality search results is essential to ensure that customers find what they’re looking for quickly and effortlessly.
How DoorDash is pushing experimentation boundaries with interleaving designs
We’ve traditionally relied on A/B testing at DoorDash to guide our decisions.
Experiment Faster and with Less Effort
Business Policy Experiments Using Fractional Factorial Designs
At DoorDash, we constantly strive to improve our experimentation processes by addressing four key dimensions, including velocity to increase how many experiments we can conduct, toil to minimize our launch and analysis efforts, rigor to ensure a sound experimental design and robustly efficient analyses, and efficiency to reduce costs associated with our experimentation efforts.