用大模型和图神经网络解决招聘推荐的冷启动与偏差问题
A Scalable and Efficient Signal Integration System for Job Matching
- 融合大模型文本理解与图神经网络关系建模能力
- 支持工业级扩展，实现高效嵌入生成与部署
- 适合大规模推荐系统研发人员参考落地
领英作为全球最大的职业社交平台，在其职位匹配推荐系统中面临冷启动、信息茧房及候选人-职位匹配偏差等建模挑战。为此，我们构建了STAR（Signal Integration for Talent And Recruiters）信号整合系统，结合大语言模型（LLMs）对文本内容（如个人资料、职位描述）的理解能力与图神经网络（GNNs）捕捉复杂关系、缓解冷启动问题的能力。STAR通过自适应采样与版本管理等工业级范式，整合多源信号，提供端到端的嵌入构建与部署方案。主要贡献包括：面向工业应用的嵌入构建方法论、可扩展的GNN-LLM融合架构，以及真实场景下模型部署的实践洞见。
原文摘要 · Abstract (English)
LinkedIn, one of the world's largest platforms for professional networking and job seeking, encounters various modeling challenges in building recommendation systems for its job matching product, including cold-start, filter bubbles, and biases affecting candidate-job matching. To address these, we developed the STAR (Signal Integration for Talent And Recruiters) system, leveraging the combined strengths of Large Language Models (LLMs) and Graph Neural Networks (GNNs). LLMs excel at understanding textual data, such as member profiles and job postings, while GNNs capture intricate relationships and mitigate cold-start issues through network effects. STAR integrates diverse signals by uniting LLM and GNN capabilities with industrial-scale paradigms including adaptive sampling and version management. It provides an end-to-end solution for developing and deploying embeddings in large-scale recommender systems. Our key contributions include a robust methodology for building embeddings in industrial applications, a scalable GNN-LLM integration for high-performing recommendations, and practical insights for real-world model deployment.
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