Idea
Hybrid keyword and embedding search platform for social networks improving relevance and engagement in group post retrieval.
Research Paper
Core Innovation
This paper introduces a hybrid retrieval system blending keyword and embedding-based methods to improve social network search relevance and diversity. It also proposes a novel LLM-based offline evaluation framework for scalable and consistent relevance assessment. This approach enhances user engagement and search quality beyond traditional keyword search.
Market Size (TAM)
$10–20B TAM for social media search and content discovery; $2–10B SAM from large social platforms and enterprise community management. Driven by increasing demand for personalized content retrieval and scalable evaluation methods.
Potential Customers & Pain Points
- Social Media Platforms Needing Improved Search Relevance
- Enterprises Managing Large Social Communities
- Developers Seeking Scalable Search Evaluation Methods
Business Model
SaaS platform offering hybrid search APIs and LLM-based evaluation tools to social media companies and enterprises managing online communities.
Competitive Landscape
- Google Search
- Microsoft Bing
- Elastic
Implementation Challenges
- Integration Complexity with Existing Systems
- Dependence on Large Language Models for Evaluation
- Scalability in Real-Time Environments
Validation Strategy
- Deploy prototype in Facebook Groups for A/B testing
- Measure user engagement and search relevance improvements
- Conduct offline LLM-based relevance assessments for quality benchmarking
Research Paper Overview
Modernizing Facebook Scoped Search: Keyword and Embedding Hybrid Retrieval with LLM Evaluation
Summary
This paper presents a modernized Facebook Group Scoped Search framework that combines keyword-based retrieval with embedding-based retrieval to enhance search relevance and diversity. It integrates semantic retrieval into the existing keyword search pipeline, enabling users to find more contextually relevant group posts. The paper also introduces a novel evaluation framework using large language models for offline relevance assessment, providing scalable and consistent quality benchmarks. Results show significant improvements in user engagement and search quality validated by online metrics and LLM-based evaluation.