Innovative Recommendation Applications Using Two Tower Embeddings at Uber

This post details the development and application of Two-Tower Embeddings (TTE) for recommendation systems at Uber, specifically for Uber Eats. It introduces the concept of embeddings and TTE, explaining their architecture (query tower and item tower) and how they are used for candidate retrieval and ranking. The post highlights the limitations of the previous city-wise Deep Matrix Factorization (DeepMF) model, emphasizing its lack of scalability and high maintenance costs. It then presents TTE as a scalable, extendable, and efficient solution, detailing its advantages in scalable labeling, localized training/inference, and feature extensibility. The post also discusses future use cases for TTE in item feed ranking and generating transferable features for other domains like rider and place embeddings.