A Scalable and Efficient Signal Integration System for Job Matching

TL;DR

The paper introduces STAR, a scalable system combining LLMs and GNNs to improve job recommendation accuracy on LinkedIn by addressing cold-start, bias, and filter bubble issues.

Contribution

It presents a novel industrial-scale GNN-LLM integration methodology for building effective embeddings in large-scale recommender systems.

Findings

Enhanced recommendation quality with integrated GNN and LLM signals

Scalable architecture supporting industrial deployment

Practical deployment insights for large-scale recommender systems

Abstract

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.