Under the Hood: Indexing and ranking in Graph Search

This post details the technical implementation of search ranking within Meta's Unicorn search engine, specifically for Graph Search. It explains how Unicorn was extended to handle search ranking by separating entity types into distinct verticals, enhancing retrieval operations, implementing query rewriting and scoring mechanisms (including forward index and result set scoring), blending results from different verticals, and supporting nested queries. The post also outlines the A/B testing framework used to measure ranking changes and describes the life cycle of a Graph Search query through both the query suggestion and search phases, highlighting the role of NLP and the Top Aggregator in processing natural language queries and constructing complex search plans.