A USEM-guided data mining and recommendation system for optimizing career paths of foreign language students
TL;DR
An employment path optimization and recommendation system for foreign language college students based on data mining and the USEM model is proposed, enabling precise alignment between foreign language talent training and market demand.
Abstract
This study proposes an employment path optimization and recommendation system for foreign language college students based on data mining and the USEM model. The USEM model covers four dimensions: subject understanding (mapped to course scores), comprehensive skills (language and cross-disciplinary abilities), personal endowment (internship duration, project complexity), and metacognition (self-assessment, career planning). An improved AHP-entropy weight method determines indicator weights, while a dynamic matching model uses word vector space technology and market demand factors for real-time position-student similarity matching. To address the cold start problem, a hybrid recommendation strategy with dynamic weight adjustment and quarterly parameter updates is applied. Empirical results show the experimental group significantly outperforms the control group in Precision@5 and NDCG@10. The first-time contract signing period is shortened by 52.8%, and salary-major correlation increases by 20% and 75.6% respectively. The system effectively shifts employment guidance from static to dynamic, enabling precise alignment between foreign language talent training and market demand.