HireMatch: An AI Recruitment Platform Based on Semantic Matching and Skill Gap Analysis

This article introduces the HireMatch project, an intelligent recruitment platform built with Next.js and Supabase. It uses the pgvector vector database to achieve semantic matching between resumes and job positions, and combines large language models (LLMs) for skill gap analysis to provide AI-driven decision support for the recruitment process. The project aims to solve problems such as low efficiency, subjective bias, and limitations of keyword matching in traditional recruitment, and build an intelligent recruitment ecosystem integrating semantic search, vector databases, and LLM reasoning.

Traditional recruitment processes have long faced issues of low efficiency and subjective bias. HR needs to manually screen massive resumes, while job seekers struggle with the difficulty of quantifying the matching degree between positions and their abilities. Although keyword matching solves some problems, it cannot understand the semantic relationship between "familiar with Python" and "proficient in Django", nor can it identify the potential value of transferable skills. HireMatch attempts to reconstruct the process using modern AI technology to create an intelligent recruitment ecosystem.

Next.js: Full-Stack React Framework

• Server-side rendering (SSR) improves first-screen loading speed and SEO;
• API routes simplify front-end and back-end communication;
• File system routing reduces development complexity;
• Incremental static regeneration (ISR) supports near-real-time updates.

Supabase: Open-Source BaaS Platform

• Provides infrastructure such as PostgreSQL database, authentication services, real-time subscriptions, and edge functions.

pgvector: Postgres Vector Extension

• Core technology that supports high-dimensional vector storage, multiple distance metrics, approximate nearest neighbor search (IVFFlat/HNSW indexes), and ACID compliance.

Semantic Resume Matching

1. Embedding Generation: Use pre-trained models (e.g., text-embedding-3, Sentence-BERT) to convert resumes and job positions into semantic vectors;
2. Vector Indexing: Store embeddings and build HNSW indexes to accelerate retrieval;
3. Similarity Retrieval: Sort results by semantic similarity;
4. Threshold Filtering: Ensure high-quality matches are returned.

Skill Gap Analysis

1. Skill Extraction: LLM identifies hard/soft skills from job descriptions;
2. Resume Parsing: Extract candidates' skills and experience;
3. Gap Mapping: Generate reports on fully matched, partially matched, missing essential skills, and bonus items by comparison;
4. Learnability Assessment: Analyze the difficulty of filling gaps to assist decision-making.

Resume Parsing Pipeline

1. Document Ingestion: Supports formats like PDF and Word;
2. Text Extraction: Preserves structural information;
3. Information Structuring: LLM extracts key fields (name, experience, skills, etc.);
4. Standardization: Unifies the expression of skill names;
5. Embedding Generation: Converts to vectors;
6. Index Storage: Writes to pgvector and builds indexes.

Job Posting Process

1. JD Parsing: Extracts fields such as job requirements;
2. Skill Demand Identification: Distinguishes between mandatory and bonus skills;
3. Semantic Enhancement: LLM generates extended descriptions to improve recall rate;
4. Matching Precomputation: Asynchronously calculates Top-K matches and caches them.

Data Privacy Protection

• Minimized Collection: Only collect necessary information;
• Encrypted Storage: Encrypt resume files and parsing results;
• Access Control: Row-level security policies;
• Data Retention: Automatically clean up expired resumes and support active deletion.

Algorithmic Fairness

• Bias Detection: Regularly audit matching results;
• Interpretive Output: Explain the reasons for recommendations;
• Manual Review: Retain manual intervention options at key nodes.

Feature	HireMatch	LinkedIn	Indeed	Traditional ATS
Semantic Matching	Native Support	Partial Support	Keyword-Based	Usually Not Supported
Skill Gap Analysis	LLM-Driven	None	None	None
Open-Source & Customizable	Yes	No	No	Partial
Data Sovereignty	Self-Hosting Optional	Platform-Hosted	Platform-Hosted	Usually Self-Hosted
Cost Structure	Infrastructure Cost	Function-Based Pricing	Exposure-Based Pricing	License Fee

HireMatch provides small and medium-sized enterprises with a customizable, self-hostable intelligent recruitment solution, avoiding lock-in by commercial platforms.

Future Development Directions

• Video Interview Analysis: Integrate audio and video processing to analyze communication and expression;
• Predictive Recruitment: Predict talent demand based on historical data;
• Multilingual Support: Expand to multilingual scenarios;
• Blockchain Verification: Explore decentralized verification of academic qualifications/experience.

Conclusion

HireMatch demonstrates the application of AI in the recruitment field. By automating repetitive tasks and providing data insights, it allows HR to focus on links such as cultural fit assessment. The combination of pgvector and LLM provides a cost-effective technical path, which is expected to be popularized in enterprise-level AI applications.