rahulrajpu676/job-resume-matching

Resume–Job Matching Dataset

Dataset Summary

This dataset is designed for research and experimentation on resume-to-job matching, semantic retrieval, ranking, and recommendation systems. It contains candidate resume records, job posting records, and candidate-job compatibility records with multiple scoring features.

The dataset can be used for tasks such as:

• —Resume-job matching
• —Candidate ranking
• —Job recommendation
• —Semantic similarity modeling
• —Skill-based matching
• —Learning-to-rank experiments
• —Graph-based recommendation models
• —Embedding-based retrieval

Dataset Files

The dataset contains three CSV files:

Data Structure

cv.csv

Each row represents one candidate profile.

job.csv

Each row represents one job posting.

matches.csv

Each row represents a candidate-job compatibility record.

Dataset Statistics

Overall Counts

Job Category Distribution

Candidate Category Distribution

Match Label Distribution

Score Ranges

Intended Use

This dataset is suitable for building and evaluating matching and recommendation systems in the recruitment domain.

Example use cases include:

1. 1.Semantic retrieval Use resume and job embeddings to retrieve relevant jobs for each candidate.

1. 1.Learning-to-rank Train a ranking model using compatibility scores, ranking positions, and label strengths.

1. 1.Classification Convert label_strength into classes for supervised classification.

1. 1.Graph recommendation Build a heterogeneous or bipartite graph using candidates, jobs, and compatibility edges.

1. 1.Hybrid matching models Combine semantic similarity, skill compatibility, position matching, category matching, experience matching, and education matching.

Example Usage

import pandas as pd

cv_df = pd.read_csv("cv.csv")
job_df = pd.read_csv("job.csv")
matches_df = pd.read_csv("matches.csv")

print(cv_df.shape)
print(job_df.shape)
print(matches_df.shape)

Load Embeddings

The embedding columns are stored as string representations of numeric lists. You can convert them back into arrays as follows:

import ast
import numpy as np

cv_df["embedding"] = cv_df["embedding"].apply(lambda x: np.array(ast.literal_eval(x), dtype=np.float32))
job_df["embedding"] = job_df["embedding"].apply(lambda x: np.array(ast.literal_eval(x), dtype=np.float32))

print(cv_df["embedding"].iloc[0].shape)
print(job_df["embedding"].iloc[0].shape)

Join Candidate, Job, and Match Data

merged_df = (
    matches_df
    .merge(cv_df, on="candidate_id", how="left")
    .merge(job_df, on="job_id", how="left", suffixes=("_candidate", "_job"))
)

merged_df.head()

Convert Match Labels to Numeric Classes

label_map = {
    "weak_positive": 0,
    "medium_positive": 1,
    "strong_positive": 2,
}

matches_df["label_id"] = matches_df["label_strength"].map(label_map)

Recommended Train/Validation/Test Splitting

For recommendation and ranking experiments, avoid random row-level splitting only. A row-level split may place records from the same candidate in multiple subsets and can overestimate model performance.

Recommended approaches:

• —Split by candidate_id for candidate-level generalization.
• —Split by job_id for job-level generalization.
• —Use a time-based split if timestamp information is added in future versions.
• —For graph-based models, ensure validation and test edges are not included in the training graph as target edges.

Data Quality Notes

• —All columns in the provided CSV files are complete, with no missing values detected.
• —Candidate and job embeddings are stored as text and should be parsed before numerical modeling.
• —The dataset contains positive compatibility records. For binary classification or pairwise ranking, negative or unobserved candidate-job pairs may need to be sampled depending on the experiment design.
• —Some candidates or jobs may not appear in matches.csv; this is expected when using a filtered compatibility table.

Ethical Considerations

Recruitment datasets should be handled carefully because automated matching systems can influence employment opportunities. Users of this dataset should:

• —Evaluate models for bias across relevant groups where such metadata is available and appropriate.
• —Avoid using protected attributes for ranking or filtering.
• —Treat model scores as decision-support signals rather than final hiring decisions.
• —Review recommendations with human oversight.
• —Ensure compliance with applicable privacy, employment, and data protection regulations.

Limitations

• —The dataset focuses on selected job categories and may not generalize to all occupations.
• —Compatibility labels and scores should be interpreted as modeling signals, not definitive hiring decisions.
• —The dataset does not guarantee complete coverage of every possible candidate-job pair.
• —The embedding model used to create the stored vectors is not specified in the dataset files.
• —Additional validation may be required before using models trained on this dataset in real recruitment workflows.

Citation

If you use this dataset in a project, report, or experiment, please cite the dataset repository.

@dataset{resume_job_matching_dataset,
  title  = {Resume-Job Matching Dataset},
  author = {Dataset Contributors},
  year   = {2026},
  url    = {https://huggingface.co/datasets/<your-username>/<your-dataset-name>}
}

License

Please update the dataset license before publishing. Use a license that matches your data source permissions and intended sharing policy.

Dataset Maintenance

For questions, updates, or corrections, please open an issue or discussion in the dataset repository.