AI-ATS: A Scalable Machine Learning and Vector Space Model Platform for Automated Resume Screening and Contextual Job Recommendation

Abstract

Contemporary recruitment systems suffer from severe friction between high-volume resume submissions and recruiter screening capacity. Conventional Applicant Tracking Systems (ATS) rely primarily on rigid exact-keyword matching heuristics, resulting in high false-rejection rates for qualified candidates who describe their technical competencies using synonymous or contextual phrasing. Conversely, candidates submit applications without diagnostic visibility into structural weaknesses or keyword deficits in their resumes. This paper presents the architecture, mathematical modeling, and empirical validation of AI-ATS, an intelligent recruitment platform integrating automated PDF text extraction, rule-weighted structural ATS scoring, high-dimensional vector-space job recommendation, and a synchronized dual-role recruitment workflow. AI-ATS parses raw binary resumes via pdfplumber, tokenizes technical skills using an optimized dictionary regex engine, and computes a piecewise ATS compatibility index across document length, skill density, and canonical structural sections. For candidate job matching, the system transforms candidate text and job descriptions into high-dimensional sparse Term Frequency-Inverse Document Frequency (TF-IDF) vectors and calculates cosine similarity across enterprise job profiles, returning ranked recommendations in under 320 ms. To bridge the candidate-recruiter gap, AI-ATS couples candidate-facing diagnostic filters with a recruiter portal supporting custom vacancy management, applicant tracking, and status progression. We formalize the candidate-job vector space model, present core algorithms for resume parsing, vector ranking, and candidate filtering, and analyze experimental results across multiple technical seniority profiles. We conclude by examining parsing edge cases, bias mitigation, and future integration with transformer-based embeddings