Empirical Evaluation of Word Representation Methods in the Context of Candidate-Job Recommender Systems
Bibliographic record
Abstract
In this paper, we have evaluated our hybrid two-way recommendation system with expert-ranked resumes and job descriptions. The aim of the paper is to compare the lists produced by the recommendation system with human-ranked lists for candidate and job descriptions. Firstly, we set up four scenarios such as the matching of resume to resumes, job to jobs, resume to jobs, and job to resumes, and prepared a human ranking based on the content similarity on a total of 400 documents. Based on this annotated corpus we tested our system to calculate the cosine-similarity-based ranking for each scenario using the Global Vectors for Word Embeddings and Term Frequency-Inverse Document Frequency representations. Finally, we compared the similarities of human ranked lists and system-ranked lists by using the Rank Biased Overlap (RBO) similarity score. In both methods, GloVe and TF-IDF, the median RBO between human-ranked lists and system ranked are greater than 0.5. The highest median score is achieved on TF-IDF with a slight difference compared to GloVe apart from the ranking of resume-to-resume scenario where the variation between the two methods is considerable. This is due to the similarity between human-ranked lists and program-generated lists.
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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 | 0.015 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".