Under the hood: Photo Search

What this post added

This post details the application of machine learning, specifically deep neural networks and image understanding engines, to enhance Facebook's Photo Search functionality. It describes the use of Unicorn, an in-memory and flash storage indexing system, to index and search trillions of photo-related entities. The post explains how high-dimensional feature vectors from deep residual networks are compressed using iterative quantization and locality-sensitive hashing to create compact embeddings for efficient retrieval and ranking. It also covers query understanding techniques, including intent recognition, syntactic analysis, entity linking, and query embedding, and outlines the two-stage ranking process (first-stage ranking and second-stage re-ranking) that leverages these signals and embeddings to deliver relevant photo search results.