DoorDash serves a vast and diverse set of merchants, with every restaurant, menu, and dish expressed in its own unique way.
Tag Archives: Search and Recommendations
Using small language models to serve more relevant DoorDash search ads
When consumers search on DoorDash, they are usually trying to accomplish something quickly.
Building a unified consumer memory for personalization at scale
DoorDash’s marketplace spans restaurants, groceries, convenience stores, retail outlets, and more.
Offline LLMs, Online Personalization: Generating carousels at DoorDash
Recommendation systems provide highly personalized results, but building hyperpersonalized experiences remains challenging because of the bottlenecks created by content generation and presentation.
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.
When GenAI Meets Personalization: Powering DoorDash’s next-generation homepage experience
At DoorDash, we strive to deliver the best shopping experience to our customers.
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.
Profile Generation with LLMs: Understanding consumers, merchants, and items
To elevate the quality of personalization, DoorDash is evolving how we represent our core entities.
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 leverages LLMs for better search retrieval
At DoorDash, users commonly conduct searches using precise queries that compound multiple requirements.