Knowledge Graph Embeddings for Job Posting Targeting

The patent replaces manual targeting rules (mechanical system) with a machine learning-based knowledge graph embedding system. The system uses unsupervised learning to train embeddings that capture semantic relationships between job postings and user profiles, automatically generating targeting labels without manual intervention. This substitution resolves the contradiction by achieving high accuracy through automated semantic matching while eliminating the need for complex manual rule configuration.

The patent implements a dynamic content delivery system where the knowledge graph embeddings are continuously updated to reflect changing job market conditions. The unsupervised learning approach allows the system to automatically adapt to new job types, skills, and market trends without requiring manual rule updates. The embeddings capture evolving semantic relationships, enabling the system to dynamically adjust targeting accuracy as the job market changes.

The patent performs preliminary action by pre-training knowledge graph embeddings on historical job posting data and user profiles before actual content delivery. The unsupervised learning process creates a rich semantic representation that captures relationships between jobs, skills, and user characteristics in advance. During runtime, the system only needs to query the pre-computed embeddings and generate labels based on similarity matching, significantly reducing real-time computational complexity while maintaining high accuracy.