An AI-Driven Placement Ecosystem for Automated Skill Matching
Bibliographic record
Abstract
One of the biggest challenges in today’s recruit- ment systems is making sure that candidate resumes match job descriptions. This matching directly affects the quality of hiring decisions and the productivity of the organization. To accomplish this, we propose a supervised fine-tuning method for semantic resume–job matching that leverages Sentence-BERT (SBERT) embeddings to match candidates to job descriptions with high accuracy. Our approach represents both resumes and job descriptions in a shared embedding space. This allows the method to use high-quality computation of similarity for the retrieval of top-k job match rankings. The model was fine- tuned and trained on a labeled dataset of resume–job pairs, and evaluated using Spearman and Pearson correlation coefficients to assess agreement with ground truth relevance, with additional metrics of top-k retrieval, namely Precision, Recall and Normalized Discounted Cumulative Gain (NDCG). Experimental results show that the fine-tuned method outperformed the pretrained baseline, achieving high correlations, precision, and accuracy in identifying relevant candidates. This work demonstrates the use of embedding, along with supervised fine- tuning, can improve accuracy and applicability of resume-job matching approaches. The experimental analysis shows that the fine-tuned model consistently gets higher performance scores than the pretrained baseline.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.010 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".