Deep Learning for College Graduates Employment Prediction: A Computational Approach

Abstract

The dynamic nature of employment systems, influenced by individual behaviors and macroeconomic factors, necessitates sophisticated computational models to inform effective policy-making. The inability of traditional statistical techniques to effectively represent temporal dynamics and market heterogeneity in labor systems hampers their value for both forecasting and optimization purposes. To address these challenges, we introduce the Stratified Adaptive Employment Network (SAEN), a deep learning architecture that models employment dynamics through stratified hierarchical learning and temporal state encoding. SAEN accounts for population heterogeneity by segmenting individuals into latent strata, each governed by tailored recurrent units that process employment histories alongside individual and macroeconomic covariates. To enhance policy relevance, we integrate the Policy-Informed Generative Optimization (PIGO) framework, which embeds policy constraints directly into the model’s optimization process, enabling the simulation of policy interventions and their potential impacts on employment outcomes. This work advances applied computational methodologies through the development of novel algorithms that address complex, real-world problems. Empirical evaluations demonstrate that the proposed framework outperforms existing methods in forecasting employment transitions and provides actionable insights for policy design, thereby contributing to the broader objectives of computational sciences in understanding and optimizing human-centric systems.

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