Machine Learning-Based Recommendations and Classification System for Unstructured Resume Documents
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
With the burgeoning growth of the job market and a surge in applications, the processes of job recommendation and candidate selection have become complex and labor-intensive.The advent of new technologies such as machine learning has automated these processes, yet the unstructured nature of resumes, often in PDF format, necessitates laborious data extraction for efficient skill-based candidate screening and categorization.Ineffectual recruitment can result from mismatched skills.The system proposed in this study aims to address these challenges by automatically fetching and categorizing resumes, extracting critical information, and utilizing job descriptions for candidate selection and recommendations.Unstructured data from PDF documents is extracted using a PDF reader, and machine learning algorithms, specifically logistic regression and Gaussian Naï ve Bayes, are employed for generating recommendations.In an innovative approach, this system not only classifies resumes but also recommends updates or rewrites.Performance of the proposed system is evaluated in terms of classification accuracy and the effectiveness of update recommendations, and results are compared with alternative models.This research represents a significant advancement in the application of machine learning to the automation of job recommendation and candidate selection processes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it