## 3 Results and Discussion
### Quantitative Results  
#### 3.4.1 Performance Metrics Comparison  
In Table 6, we present the Top-K Accuracy results for all three models (GRU, LSTM, and Transformer) evaluated on the training, validation, and test sets. These accuracies are reported for various values of \(K\), allowing us to examine the ranking quality at different cutoff thresholds. Specifically, Top-1 and Top-3 provide insight into how often the very best matches were correctly identified, whereas larger \(K\) values (such as Top-50 or Top-100) offer a broader perspective on how well the models performed when returning an extended list of potential candidates. Table 7 complements the Top-K Accuracy results by showing additional performance metrics, such as the _Mean Reciprocal Rank_ (MRR), the average similarities between job and positive (matching) resumes, the average similarities for job and negative (non-matching) resumes, and the final loss values. MRR places particular emphasis on the position of \begin{table}
\begin{tabular}{l c} \hline \hline
**Parameter Category** & **Value** \\ \hline _Sentence Transformer_ & \\ Base Model & distiluse-base-multilingual-cased-v2 \\ Input Embedding Size & 512 \\ \hline _Head Model (GRU/LSTM/Transformer)_ & \\ Hidden Size & 256 \\ Number of Layers & 1 \\ Output Embedding Size & 256 \\ Output Aggregation & attention \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Model architecture and embedding configuration parameters. the first correct candidate, thereby reflecting how promptly a model can retrieve a valid match. The average positive vs. negative similarities indicate how effectively each model managed to separate relevant resumes from irrelevant ones in the embedding space. Finally, the reported loss values show how consistently each model converged during training and how well it generalized on unseen data. GRU Performance. Based on Table 6, the GRU model achieved robust performance across small \(K\) ranges (Top-1 and Top-3). This suggests that it excels at identifying the single most relevant candidate. This effect is also reflected in its high MRR (Table 7), indicating that correct candidates often appear near the top of its ranked lists. The GRU's recurrent gating mechanism helps capture job requirements and candidate experience in a sequential manner, which is beneficial when dealing with moderately sized text segments. Moreover, the noticeable gap between average positive and negative similarities (shown in Table 7) suggests that the GRU effectively learns to cluster relevant and irrelevant resumes. However, its loss curve tended to stabilize at a slightly higher value than the Transformer, implying that while GRU is strong in identifying the best match early, it may have a slightly harder time achieving consistently low overall error across a broad range of candidates. LSTM Performance. The LSTM model often displayed comparable or improved Top-K Accuracy scores (Table 6) relative to GRU for \(K\) values in the mid-range (e.g., Top-10 and Top-20). LSTM's long-term memory cells captures more extended patterns in the text, making it particularly beneficial for lengthy resumes with nuanced skills or for job descriptions containing extensive requirements. Table 7 shows that the LSTM also achieved a high MRR and maintained a discernible gap between its average positive and negative similarities, demonstrating good discriminative power. However, the average negative similarity was sometimes slightly higher than with GRU, suggesting that while LSTM excels in correctly ranking positive matches, it may occasionally place some negative resumes closer to the positive ones in the embedding space. Despite this, the overall loss for LSTM remained at a level that indicates strong convergence and effective learning of job-candidate relationships. Transformer Performance.