Table 5.
Performance of CNN-based models and LSTM-based models in Ablation Study
Systems | Components | Evaluation Metrics | ||||
---|---|---|---|---|---|---|
CW | UE | CA | Pre. | Rec. | F1 | |
LSTM-Vanilla | 0.6173 | 0.407 | 0.4335 | |||
LSTM-WPE | ✓ | 0.6376 | 0.4344 | 0.4503 | ||
LSTM-WPEU | ✓ | ✓ | 0.6064 | 0.5001 | 0.4896 | |
LSTM-NEAT | ✓ | ✓ | ✓ | 0.6197 | 0.5134 | 0.5064 |
CNN-Vanilla | 0.7214 | 0.5503 | 0.5637 | |||
CNN-WPE | ✓ | 0.7423 | 0.5799 | 0.5804 | ||
CNN-WPEU | ✓ | ✓ | 0.6923 | 0.6350 | 0.5910 | |
CNN-NEAT | ✓ | ✓ | ✓ | 0.7066 | 0.6431 | 0.6139 |
In the Components column, CW, UE, CA denote Credibility Weights, User Expertise and Cluster Attention module components, respectively. In the Evaluation Metrics column, Pre., Rec. and F1 denote Precision, Recall, and F1 score