融合关键词与向量检索，提升社交搜索相关性与多样性。
Modernizing Facebook Scoped Search: Keyword and Embedding Hybrid Retrieval with LLM Evaluation
- 结合关键词与嵌入向量检索，增强结果相关性。
- 混合检索使用户参与度显著提升，效果经线上指标验证。
- 用大模型评估搜索质量，适合大规模社交平台部署。
除通用网络搜索外，社交网络搜索还能帮助用户在其社交关系中发现信息和潜在联系。我们提出一个现代化的 Facebook 群组范围搜索框架，将传统关键词检索与嵌入式检索（EBR）相结合，以提升搜索结果的相关性与多样性。系统将语义检索集成到现有关键词搜索流程中，使用户能发现更相关的群组内容。为严格评估该混合方法的影响，我们引入一种基于大语言模型（LLM）的新型离线评估框架，实现可扩展且一致的质量基准。结果显示，混合检索系统显著提升了用户参与度与搜索质量，经线上指标与 LLM 评估双重验证。本工作为在大规模真实社交平台部署和评估先进检索系统提供了实用洞见。
原文摘要 · Abstract (English)
Beyond general web-scale search, social network search uniquely enables users to retrieve information and discover potential connections within their social context. We introduce a framework of modernized Facebook Group Scoped Search by blending traditional keyword-based retrieval with embedding-based retrieval (EBR) to improve the search relevance and diversity of search results. Our system integrates semantic retrieval into the existing keyword search pipeline, enabling users to discover more contextually relevant group posts. To rigorously assess the impact of this blended approach, we introduce a novel evaluation framework that leverages large language models (LLMs) to perform offline relevance assessments, providing scalable and consistent quality benchmarks. Our results demonstrate that the blended retrieval system significantly enhances user engagement and search quality, as validated by both online metrics and LLM-based evaluation. This work offers practical insights for deploying and evaluating advanced retrieval systems in large-scale, real-world social platforms.
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