## 5 Experiments and Discussion
### Comparative Evaluation and Analysis of Performance
#### 5.3.3 Influence of Hyperparameters  
EIAFM configures both the DNN and CIN with two hidden layers, and each hidden layer has an equal number of units. Only the number of units in each hidden layer needs to be investigated, as the dropout rates for the DNN, CIN, attention network output, and output of the pair-wise interaction layer are uniformly configured. All other hyperparameters were configured using their default values when exploring the effects of each parameter on the EIAFM model. Figure 4 shows the impact of the hyperparameters of EIAFM (number of DNN hidden units, attention factor, L2 regularization strength, dropout rate, and number of CIN hidden units). 1. The effect of the quantity of hidden units in the DNN: as observed in in Figure 4a,f, it can be seen that the log-loss value decreases sharply with the increase in the number of DNN hidden units; when the count of hidden units reaches 64, the log-loss value starts to rise sharply. The corresponding AUC value first rises sharply, then starts to \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Models** & **Complexity** & **Parameter Count** & **Runtime (Second)** \\ \hline basicFM & O(NE) & \(3\times 10^{3}\) & 1.0754 \\ \hline AFM & O(NE+N\({}^{2}\)E+EA) & \(4\times 10^{6}\) & 10.2260 \\ \hline DeepFM & O(NE+NEHU) & \(1\times 10^{5}\) & 2.5560 \\ \hline xDeepFM & O(NE+NEHU+NTF\({}^{2}\)) & \(1\times 10^{5}\) & 3.6041 \\ \hline EIAFM & O(NE+N\({}^{2}\)E+EHU+EA+NTF\({}^{2}\)) & \(4\times 10^{6}\) & 14.0392 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Complexity, parameter count, and runtime comparison of FM-based models. fall when the count of hidden units reaches 64. More hidden units make the model more capable of nonlinear interactions. However, an excessive number of hidden units can lead to overfitting in the model. When the number of hidden units is in the range of [32,128], both the log-loss and AUC perform well. 2. Effect of Attention Factor: from Figure 4b,g, it can be observed that the log-loss and AUC fluctuate slightly when the attention factor is greater than 32. This fluctuation is due to the fact that the role of the attention component is to use the attention weights to measure which feature interactions contribute more significantly to the prediction. For low attention factors, there exists only slight variability in the significance scores of various feature interactions. The log-loss and AUC fluctuate relatively little when the attention factor reaches 32, and overall the model works best at an attention factor of 32. 3. Effect of L2 regularization and dropout rate: from Figure 4c,h, it can be observed that the log-loss decreases first and then increases, and the model achieves the best performance when the strength of L2 regularization is \(1\times 10^{-3}\). The main purpose of L2 regularization is to avoid overfitting. The optimization of the loss function is affected when the L2 regularization reaches \(1\times 10^{-1}\); the overall view is that the best results for both the log-loss and AUC are achieved when the strength of L2 regularization is \(1\times 10^{-3}\). The effect of the dropout rate can be observed from Figure 4d,i; the effect of dropout rate is not very large for the model except for the setting of 0.7. The log-loss and AUC values perform poorly, as on average a larger dropout requires more iterations for the model training to converge for the experimental default number of iterations. The model exhibits peak performance when the dropout rate is configured as 0.5. 4. The effect of the quantity of hidden layer units in CIN: as can be observed from Figure 4e,j, the log-loss first decreases and then grows drastically with the increase in the number of CIN hidden units. The reason for this is mainly that the increase in the quantity of neurons in each layer of the CIN results in an increase in the number of feature maps in the CIN.