What is two-tower model in recommendation systems?

A two-tower model learns separate embeddings for users and items and matches them via similarity.

One neural network encodes users and another encodes items. Their embeddings are compared using dot product or cosine similarity. This architecture is scalable for large-scale retrieval systems like YouTube and Google Search recommendations.