Modernizing Facebook Scoped Search: Keyword and Embedding Hybrid Retrieval with LLM Evaluation

• URL: http://arxiv.org/abs/2509.13603v1
• Date: Wed, 17 Sep 2025 00:22:08 GMT
• Title: Modernizing Facebook Scoped Search: Keyword and Embedding Hybrid Retrieval with LLM Evaluation
• Authors: Yongye Su, Zeya Zhang, Jane Kou, Cheng Ju, Shubhojeet Sarkar, Yamin Wang, Ji Liu, Shengbo Guo,
• Abstract summary: We introduce a framework of modernized Facebook Group Scoped Search.<n>We blend traditional keyword-based retrieval with embedding-based retrieval to improve the search relevance and diversity of search results.<n>Our results demonstrate that the blended retrieval system significantly enhances user engagement and search quality.
• Score: 4.821105793289088
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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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