A Hybrid Machine Learning Approach for Graduate Admission Prediction and Combined University-Program Recommendation
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866918420612644864 |
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| author | Far, Melina Heidari Tabrizi, Elham |
| author_facet | Far, Melina Heidari Tabrizi, Elham |
| contents | Graduate admissions have become increasingly competitive. This study highlights the need for a hybrid machine learning framework for graduate admission prediction, focusing on high-quality similar applicants and a recommendation system. The dataset, collected and enriched by the authors, includes 13,000 self-reported GradCafe application records from 2021 to 2025, enriched with features from the OpenAlex API, QS World University Rankings by Subject, and Wikidata SPARQL queries. A hybrid model was developed by combining XGBoost with a residual refinement k-nearest neighbors module, achieving 87\% accuracy on the test set. A recommendation module, then built on the model for rejected applicants, provided targeted university and program alternatives, resulting in actionable guidance and improving expected acceptance probability by 70\%. The results indicate that university quality metrics strongly influence admission decisions in competitive applicant pools. The features used in the study include applicant quality metrics, university quality metrics, program-level metrics, and interaction features. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_29881 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | A Hybrid Machine Learning Approach for Graduate Admission Prediction and Combined University-Program Recommendation Far, Melina Heidari Tabrizi, Elham Information Retrieval Machine Learning Graduate admissions have become increasingly competitive. This study highlights the need for a hybrid machine learning framework for graduate admission prediction, focusing on high-quality similar applicants and a recommendation system. The dataset, collected and enriched by the authors, includes 13,000 self-reported GradCafe application records from 2021 to 2025, enriched with features from the OpenAlex API, QS World University Rankings by Subject, and Wikidata SPARQL queries. A hybrid model was developed by combining XGBoost with a residual refinement k-nearest neighbors module, achieving 87\% accuracy on the test set. A recommendation module, then built on the model for rejected applicants, provided targeted university and program alternatives, resulting in actionable guidance and improving expected acceptance probability by 70\%. The results indicate that university quality metrics strongly influence admission decisions in competitive applicant pools. The features used in the study include applicant quality metrics, university quality metrics, program-level metrics, and interaction features. |
| title | A Hybrid Machine Learning Approach for Graduate Admission Prediction and Combined University-Program Recommendation |
| topic | Information Retrieval Machine Learning |
| url | https://arxiv.org/abs/2603.29881 |