## 3 Results and Discussion
### Quantitative Results
#### 3.4.2 Training Dynamics
Figure 7: Transformer—average training loss vs. epoch. Figure 8: GRU—validation loss vs. epoch. Figure 10: Transformer—validation loss vs. epoch. Figure 9: LSTM—validation loss vs. epoch. For the GRU network (Figure 8), the loss plunged from a relatively high initial level to roughly 0.03-0.04 over the first few dozen epochs, followed by only minor oscillations. A similar pattern emerged in the LSTM model (Figure 9), where there was also a marked early decline and subsequent leveling off, reflecting how the LSTM's gating mechanism adeptly captured temporal or sequential relationships in the text. In the case of the Transformer (Figure 10), we can observe a higher initial loss peak that quickly dropped, followed by a stabilization phase at a comparably low level. The self-attention mechanism underlying the Transformer model enabled it to efficiently represent long-range dependencies between elements of the job descriptions and candidate resumes. Across all three plots, the quick descent in validation loss indicates effective triplet loss minimization, whereas the final plateaus suggest that each architecture learned a robust representation for job-candidate matching. Subtle differences among the curves reflect variations in how recurrent networks (GRU and LSTM) and the self-attention approach (Transformer) capture the relevant semantic and structural features within the recruitment data. The GRU head trimmed the validation loss from \(\sim\)0.14 to \(\sim\)0.04 inside the first ten epochs, mirroring its fast training set convergence. Between epochs 50 and 250, the curve bottomed out near 0.030, after which a slow up-turn appeared, and the loss drifted toward 0.045 at epoch 1000. That rebound suggests that the model started to overfit once the margin had been maximized, a behavior also observed by [PERSON] et al. [39]. Early stopping around epoch 250 would therefore preserve the best generalization for GRU. The LSTM followed a comparable trajectory: An initial plunge from \(\sim\)0.15 to \(\sim\)0.04, then a gradual decline that plateaued between 0.030 and 0.035 until roughly epoch 500. Afterwards, the curve climbed modestly, finishing close to 0.042. The shallower U-shape compared to GRU indicates that the LSTM's larger memory cell delayed overfitting, yet the upward tail confirms the need for a patience-based stopping criterion, as recommended by [PERSON] et al. [32]. Self-attention was the fastest one in reaching its minimum: The loss collapsed from 0.065 to below 0.030 within 25 epochs and oscillated narrowly (0.028-0.033) for the remainder of training. Although a slight positive drift is still visible after epoch 600, the slope is milder than for the recurrent heads, signaling stronger generalization margins. Consequently, the Transformer could train longer without severe overfitting, but epochs beyond 300 yielded only marginal improvement. Figure 11 presents the validation accuracy of the GRU-based Siamese model as training progressed, showing a rapid initial increase followed by a smoother improvement phase, which indicates that the GRU mechanism quickly captured core similarities between job descriptions and candidate resumes. Figure 12 depicts the corresponding validation accuracy curve for the LSTM model, where the network also demonstrated strong early gains but with a more gradual convergence, reflecting its capacity to handle longer sequential dependencies in the text. Finally, Figure 13 illustrates the validation accuracy of the Transformer-based approach, revealing a relatively stable trajectory and consistently high accuracy later in training, suggesting that its attention mechanisms are particularly effective at extracting nuanced features from both job and resume texts to distinguish suitable candidates from less relevant ones. The GRU surpassed 80% Top-100 accuracy within the first \(\sim\)50 epochs, demonstrating that the network learned to surface the most relevant candidates very quickly.