用混合模型预测研究生录取，为落选者推荐合适学校与专业。
A Hybrid Machine Learning Approach for Graduate Admission Prediction and Combined University-Program Recommendation
- 结合XGBoost与残差修正的KNN，提升录取预测精度。
- 测试集准确率达87%，推荐系统使录取概率提升70%。
- 适合申请失败者或想优化申请策略的人参考。
研究生入学竞争日益激烈。本研究提出一种混合机器学习框架，用于研究生录取预测及院校-项目联合推荐。数据集由作者收集并扩充，包含2021至2025年13,000条来自GradCafe的自报申请记录，并通过OpenAlex API、QS世界大学学科排名和Wikidata SPARQL查询补充特征。研究构建了融合XGBoost与残差修正k近邻模块的混合模型，在测试集上达到87%准确率。基于该模型，为被拒申请人开发了推荐模块，提供针对性的院校与项目替代方案，显著提升预期录取概率70%。结果表明，在竞争性申请群体中，高校质量指标对录取决策有显著影响。所用特征包括申请人质量指标、高校质量指标、项目层级指标及交互特征。
原文摘要 · Abstract (English)
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.
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