## 2 Materials and Methods
### Evaluation Metrics
#### 2.5.1 Ranking Metrics  
To quantify the model's ability to rank relevant candidates higher than irrelevant ones for a given job description, we applied Top-K Accuracy and Mean Reciprocal Rank (MRR), which are described as follows:  
* Top-K Accuracy: Measures the percentage of times a correct (positive or soft positive) candidate resume appears within the top-K-ranked resumes for its corresponding job description. We calculated this for K values of 1, 3, 5, 10, 20, 30, 50, 75, and 100. The comparative results are presented in Table 6. \begin{table}
\begin{tabular}{l c} \hline
**Parameter Category** & **Value** \\ \hline _Training Hyperparameters_ & \\ Batch Size & 1024 \\ Number of Epochs & 1000 \\ Learning Rate & 0.001 \\ Triplet Loss Margin (\(\alpha\)) & 0.4 \\ Gradual Triplet Scaler & 4.0 \\ \hline _Evaluation and Logging_ & \\ Top-K Accuracy Metrics (K values) & 1, 3, 5, 10, 20, 30, 50, 75, 100 \\ t-SNE Visualization Sample Size & 1000 \\ Log Frequency (steps) & Dynamic (e.g., every 1/10 epoch) \\ K Values for Plotting Accuracy & 1, 20, 50, 75, 100 \\ Main Model Save File Name & head\_model.pt \\ Best Model Save File Name & best\_model.pt \\ \hline _Data Handling and Preprocessing_ & \\ Validation Set Ratio & 0.1 \\ Embedding Chunk Lengths & 1024 \\ Max Segments per Document & 15 \\ \hline \end{tabular}
\end{table} 
Table 4: Training hyperparameters, evaluation settings, data handling, and preprocessing details. \begin{table}
\begin{tabular}{l c c c} \hline
**Parameter** & **GRU** & **LSTM** & **Transformer** \\ \hline Total Run Duration (seconds) & 3048.03 & 2903.50 & 3017.77 \\ Avg. Epoch Duration (seconds) & 1.90 & 1.73 & 1.83 \\ Device Used & cuda & cuda & cuda \\ Num GPU Used & 2 & 2 & 2 \\ NVIDIA & NVIDIA & NVIDIA & NVIDIA \\ GPU Name & GH200 144G & GH200 144G & GH200 \\  & HBM3e & HBM3e & 144G HBM3e \\ GPU CUDA Version & 12.6 & 12.6 & 12.6 \\ Num Dataloader Workers (CPU) & 144 & 144 & 144 \\ \hline \end{tabular}
\end{table} 
Table 5: Computational setup and main hyperparameters for model training. * Mean Reciprocal Rank (MRR): Calculates the average of the reciprocal ranks of the first correct candidate resume for each job description. A higher MRR indicates that the correct candidate is typically found closer to the top of the ranked list. The MRR values are included in Table 7. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Metric** & **GRU** & **LSTM** & **Transformer** \\ \hline Train Top-1 & 1.45 & 0.94 & 1.15 \\ Train Top-3 & 3.98 & 2.86 & 3.31 \\ Train Top-5 & 6.09 & 4.35 & 5.19 \\ Train Top-10 & 9.98 & 7.49 & 8.52 \\ Train Top-20 & 15.93 & 12.19 & 13.67 \\ Train Top-30 & 20.72 & 15.83 & 17.85 \\ Train Top-50 & 28.04 & 22.51 & 24.47 \\ Train Top-75 & 35.03 & 28.78 & 31.07 \\ Train Top-100 & 40.52 & 34.43 & 36.11 \\ \hline Valid Top-1 & 5.46 & 4.72 & 4.79 \\ Valid Top-3 & 12.34 & 11.13 & 11.53 \\ Valid Top-5 & 17.94 & 15.85 & 17.19 \\ Valid Top-10 & 29.53 & 24.68 & 26.10 \\ Valid Top-20 & 42.35 & 37.90 & 38.17 \\ Valid Top-30 & 49.36 & 48.01 & 47.47 \\ Valid Top-50 & 61.83 & 59.81 & 60.28 \\ Valid Top-75 & 72.69 & 69.93 & 70.67 \\ Valid Top-100 & 79.30 & 78.08 & 77.68 \\ \hline Test Top-1 & 5.00 & 5.40 & 4.50 \\ Test Top-3 & 12.80 & 13.20 & 11.60 \\ Test Top-5 & 18.10 & 19.30 & 18.00 \\ Test Top-10 & 30.90 & 29.50 & 29.90 \\ Test Top-20 & 44.70 & 43.50 & 43.40 \\ Test Top-30 & 55.20 & 52.50 & 54.60 \\ Test Top-50 & 69.20 & 66.40 & 69.20 \\ Test Top-75 & 80.00 & 78.00 & 80.50 \\ Test Top-100 & 86.70 & 84.80 & 87.20 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Top-K Accuracy comparison for GRU, LSTM, and Transformer models across train, validation, and test sets.