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.

本研究探讨如何将基于嵌入式检索的技术应用于 Facebook 搜索系统，介绍了用于建模语义嵌入的统一嵌入框架和在基于倒排索引的典型搜索系统中提供嵌入式检索的系统，以及整个系统的端到端优化技巧和经验。该方法可显著改善 Facebook 搜索垂直领域的度量标准。