# Research on Multimodal Prediction of E-Commerce Customer Satisfaction Driven by Big Data
## 4 Analysis and Testing
### Multimodal Learning  
The multimodal model was trained for 500 epochs using the Keras learning framework and the Adam optimizer. The results are shown in Table 9. The training loss decreased from 8.8611 to 0.5283, and the training accuracy (mean absolute error) improved from 2.4433 to 0.4083. The test loss was 3.3166 and the test accuracy was 1.0464, indicating a significant improvement in learning performance compared to the unimodal models. Using the Keras learning framework and the RMSProp optimizer with a learning rate of 0.001, the multimodal model was trained for 500 epochs. The results are shown in Table 10. The training loss decreased from 5.8074 to 0.5500, and the training accuracy (mean absolute error) improved from 2.0678 to 0.4818. The test loss was 3.3702 and the test accuracy was 1.0887, indicating good learning performance. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{1}{c}{\multirow{2}{*}{**Training Epochs**}} & \multirow{2}{*}{**Training Time**} & \multicolumn{2}{c}{**Training Loss**} & \multicolumn{2}{c}{**Training Accuracy**} \\  & & **(MSE) Mean** & **(MAE) Mean** \\  & & **Squared Error** & **Absolute Error** \\ \hline Epoch 1/500 & 689/689 & \multirow{2}{*}{\(-\)0 s 164 us/step} & \multirow{2}{*}{-loss: 6.4326} & \multirow{2}{*}{-mae: 2.2166} \\  & [===========] & & & \\ Epoch 2/500 & 689/689 & \multirow{2}{*}{\(-\)0 s 50 us/step} & \multirow{2}{*}{-loss: 4.9162} & \multirow{2}{*}{-mae: 1.8606} \\ Epoch 3/500 & 689/689 & \multirow{2}{*}{\(-\)0 s 55 us/step} & \multirow{2}{*}{-loss: 4.5942} & \multirow{2}{*}{-mae: 1.7759} \\ Epoch 4/500 & 689/689 & \multirow{2}{*}{\(-\)0 s 56 us/step} & \multirow{2}{*}{-loss: 4.4481} & \multirow{2}{*}{-mae: 1.7342} \\ Epoch 5/500 & 689/689 & \multirow{2}{*}{\(-\)0 s 49 us/step} & \multirow{2}{*}{-loss: 4.2125} & \multirow{2}{*}{-mae: 1.6909} \\ Epoch 496/500 & 689/689 & \multirow{2}{*}{\(-\)0 s 45 us/step} & \multirow{2}{*}{-loss: 1.8812} & \multirow{2}{*}{-mae: 0.9679} \\ Epoch 497/500 & 689/689 & \multirow{2}{*}{\(-\)0 s 39 us/step} & \multirow{2}{*}{-loss: 1.9394} & \multirow{2}{*}{-mae: 0.9694} \\ Epoch 498/500 & 689/689 & \multirow{2}{*}{\(-\)0 s 38 us/step} & \multirow{2}{*}{-loss: 1.8735} & \multirow{2}{*}{-mae: 0.9542} \\ Epoch 499/500 & 689/689 & \multirow{2}{*}{\(-\)0 s 43 us/step} & \multirow{2}{*}{-loss: 1.7950} & \multirow{2}{*}{-mae: 0.9267} \\ Epoch 500/500 & 689/689 & \multirow{2}{*}{\(-\)0 s 39 us/step} & \multirow{2}{*}{-loss: 1.8072} & \multirow{2}{*}{-mae: 0.9188} \\ \cline{3-3} \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{1}{c}{**Training Epochs**} & \multicolumn{1}{c}{**Training Time**} & \
\begin{tabular}{c} **Training Loss** \\ **(MSE) Mean** \\ **Squared Error** \\ \end{tabular} &
\begin{tabular}{c} **Training Accuracy** \\ **(MAB) Mean** \\ **Absolute Error** \\ \end{tabular} \\ \hline Epoch 1/500 & 689/689 & & & \\  & [===========] & \(-\)0 s 138 us/step & & -loss: 5.8074 & -mae: 2.0678 \\ Epoch 2/500 & 689/689 & & & -loss: 4.7239 & -mae: 1.8163 \\ Epoch 3/500 & 689/689 & & & -loss: 4.2364 & -mae: 1.7059 \\ Epoch 4/500 & 689/689 & & & -loss: 4.1844 & -mae: 1.6532 \\ Epoch 5/500 & 689/689 & & & -loss: 4.1077 & -mae: 1.6797 \\ Epoch 496/500 & 689/689 & & & -loss: 4.1060 & -mae: 1.6979 \\ Epoch 5/500 & 689/689 & & & -loss: 0.6323 & -mae: 0.5014 \\ Epoch 497/500 & 689/689 & & & -loss: 0.5574 & -mae: 0.4812 \\ Epoch 498/500 & 689/689 & & & -loss: 0.5874 & -mae: 0.4866 \\ Epoch 500/500 & 689/689 & & & -loss: 0.5736 & -mae: 0.4876 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Learning results of multimodal model using RMSProp.