Embedding-based Retrieval in Facebook Search

TL;DR

This paper explores the adaptation of embedding-based retrieval (EBR) for Facebook Search, integrating social graph context into personalized search results and demonstrating significant online performance improvements.

Contribution

It introduces a unified embedding framework and system architecture for Facebook Search, enabling effective EBR integration and optimization in a large-scale social search environment.

Findings

Significant metrics gains in online A/B experiments

Effective modeling of social graph context in embeddings

End-to-end system optimization techniques

Abstract

Search in social networks such as Facebook poses different challenges than in classical web search: besides the query text, it is important to take into account the searcher's context to provide relevant results. Their social graph is an integral part of this context and is a unique aspect of Facebook search. While embedding-based retrieval (EBR) has been applied in eb search engines for years, Facebook search was still mainly based on a Boolean matching model. In this paper, we discuss the techniques for applying EBR to a Facebook Search system. We introduce the unified embedding framework developed to model semantic embeddings for personalized search, and the system to serve embedding-based retrieval in a typical search system based on an inverted index. We discuss various tricks and experiences on end-to-end optimization of the whole system, including ANN parameter tuning and full-stack optimization. Finally, we present our progress on two selected advanced topics about modeling. We evaluated EBR on verticals for Facebook Search with significant metrics gains observed in online A/B experiments. We believe this paper will provide useful insights and experiences to help people on developing embedding-based retrieval systems in search engines.