Even state-of-the-art classifiers cannot achieve 100% precision.
Category Archives: Optimization
Using Gamma Distribution to Improve Long-Tail Event Predictions
For DoorDash, being able to predict long-tail events related to delivery times is critical to ensuring consumers’ orders arrive when expected.
Using Triplet Loss and Siamese Neural Networks to Train Catalog Item Embeddings
Understanding the contents of a large digital catalog is a significant challenge for online businesses, but this challenge can be addressed using self-supervised neural network models.
Using ML and Optimization to Solve DoorDash’s Dispatch Problem
DoorDash delivers millions of orders every day with the help of DeepRed, the system at the center of our last-mile logistics platform.
Predicting Marketing Performance from Early Attribution Indicators
DoorDash uses machine learning to determine where best to spend its advertising dollars, but a rapidly changing market combined with frequent delays in data collection hampered our optimization efforts.
Iterating Real-time Assignment Algorithms Through Experimentation
DoorDash operates a large, active on-demand logistics system facilitating food deliveries in over 4,000 cities.
Optimizing DoorDash’s Marketing Spend with Machine Learning
Over a hundred years ago, John Wanamaker famously said “Half the money I spend on advertising is wasted; the trouble is, I don’t know which half”.
Next-Generation Optimization for Dasher Dispatch at DoorDash
At DoorDash, our logistics team focuses on efficiently fulfilling high quality deliveries.
Personalized Cuisine Filter
The consumer shopping experience is a key focus area at DoorDash.