# Talent Supply and Demand Matching Based on Prompt Learning and the Pre-Trained Language Model
## 3 Results and Discussion  
Feature Embedding Extracted by the BERT Language Model Was Effective in Accounting for Talent Supply and Demand  
To evaluate the effectiveness of text feature embeddings derived from the BERT language model, we conducted a series of feature ablation studies. These studies compare BERT embeddings with those generated using Word2 Vec [47] and GloVe [48]. Specifically, we utilized a pre-trained Word2 Vec model trained on Chinese Wikipedia [49] data, alongside a pre-trained GloVe model based on a subset of Wikipedia, to generate the feature representations, respectively. The results of comparative experiments are presented in Table 2.  
As shown in Table 2, for the recommendation task, the feature embeddings based on the BERT model yielded the best results, achieving an accuracy of 82.46%, an M-Pre of 80.40%, an M-Rec of 77.15%, and an M-F1 of 77.48%. In terms of the M-F1 metric, the BERT-based features outperformed those of Word2 Vec and GloVe by 2.42% and 1.59%, respectively. For the prediction task, the model using BERT-derived features achieved an accuracy of 78.39%, M-Pre of 77.47%, M-Rec of 72.29%, and M-F1 of 73.21%, surpassing Word2 Vec and GloVe by at least 2.06%, 2.14%, 0.79%, and 1.52%, respectively.  
Although all models utilize pre-trained embedding, BERT is built on the transformer architecture, which is particularly effective in capturing global contextual relationships between sentences. This capability significantly enhances the prediction performance in downstream tasks.  
Additionally, as shown in Figure 2, we present the detailed prediction results of the TSDM model across various disciplinary categories. The results reveal that, in the recommendation task, the highest talent supply and demand matching performance was observed in the Life and Medical disciplines, with F1-scores of 92.00% and 91.04%, respectively. In contrast, the Mathematical discipline achieved the lowest F1-score, at only 56.23%. In the prediction task, the medical discipline also exhibited the highest performance, with an F1-score of 90.03%, while the Mathematical discipline achieved the lowest F1-score at 52.12%. The superior performance in the Life and Medical disciplines can be attributed to the larger training set sizes, which allow the model to better learn the relationships between talent skills and demand within these fields. The low prediction performance of the Mathematical discipline may be due to the more fragmented nature of knowledge within the mathematical field, which makes it more difficult for the model to capture common patterns.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Mission Type** & **Models** & **Acc (\%)** & **M-Pre (\%)** & **M-Rec (\%)** & **M-F1 (\%)** \\ \hline \multirow{3}{*}{Recommendation task} & Word2 Vec & 77.15 & 76.02 & 74.89 & 75.06 \\ \cline{2-6}  & GloVe & 79.40 & 77.34 & 75.45 & 75.89 \\ \cline{2-6}  & BERT & 82.46 & 80.40 & 77.15 & 77.48 \\ \hline \multirow{3}{*}{Prediction task} & Word2 Vec & 72.32 & 70.01 & 70.03 & 70.96 \\ \cline{2-6}  & GloVe & 76.33 & 75.33 & 71.50 & 71.69 \\ \cline{1-1} \cline{2-6}  & BERT & 78.39 & 77.47 & 72.29 & 73.21 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance comparison under different feature embedding models.