PotentialNet: Explainable Employee Potential Prediction via Multi-View Representation Learning
DOI:
https://doi.org/10.54097/bgmh8n87Keywords:
Employee potential prediction, human resource analytics, multi-view learning, contrastive learning, explainable artificial intelligence, tabular dataAbstract
Employee potential assessment is a high-impact human resource analytics problem, but it differs from promotion prediction because potential is prospective, multi-dimensional, and rarely available as a clean public label. This paper proposes PotentialNet, an explainable multi-view representation learning framework that separates employee attributes into profile, performance, growth, and behavior views. Dedicated view encoders learn view-specific representations, a contrastive alignment objective encourages agreement across complementary views of the same employee, and an attention fusion module produces both a high-potential probability and a view-level explanation. Because public data with verified potential labels was not available, we construct a fully reproducible semi-synthetic HR benchmark rather than inventing real employee data. Experiments compare PotentialNet with logistic regression, random forest, gradient boosting, multilayer perceptron, and LightGBM. Results show that PotentialNet is competitive with strong tabular baselines while providing interpretable view-level explanations and counterfactual action audits. The study is deliberately conservative: it avoids claims of real-world deployment validity and releases all code, generated data, figures, and raw results to support transparent reproduction.
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