Summary of Multi-Layered Matching Architecture

The multi-layered matching architecture uses three layers to match candidates and jobs:

The Three Layers

1. Semantic Layer (LLM embeddings)
   • Uses Large Language Model embeddings to capture meaning and relationships in unstructured text (resumes, job descriptions)
   • Moves beyond keyword matching to compare concepts
2. Structured Layer (Token-level embeddings, feature engineering)
   • Extracts interpretable attributes: skill proficiency, recency of experience, career progression, industry alignment
   • Provides transparency and fine-grained control
3. Temporal Layer (RNNs, Temporal Graph Neural Networks)
   • Models dynamic career trajectories and predicts future potential
   • Identifies shifts in skills and roles over time

Key Components

• Semantic Embedding Engine: Converts - text to high-dimensional vectors using fine-tuned LLMs (e.g., BERT), then uses cosine similarity for matching
• Structured Feature Extraction: Analyzes skill overlap (broad alignment and recent usage), seniority/title progression, and industry/company similarity
• Career Trajectory Modeling: Predicts future roles by modeling sequences of (user, position, company, timestamp) to identify candidates on growth trajectories

Together, these layers combine semantic understanding, structured analysis, and temporal dynamics for more accurate and predictive job matching.