# Location-Aware Deep Interaction Forest for Web Service QoS Prediction
## 4 Experiment
### The Impact of Highest Feature Interaction Order  
By comparing the prediction performance under the different highest feature interaction order, this experiment evaluates the effect of the highest feature interaction order in the scan mechanism on the prediction performance. Here, the word-embedding dimension in our experiment is set as 50 dimensions, then the highest interaction order in local features will be set as 2, 3, 4, and 5, respectively. It will greatly increase the amount of computation in the model and improve the running time of the model, so only the comparison experiments of order 2-5 are selected. LDIF-2 in the methods in Tables 8 and 9 represents the LDIF approach with the highest order of 2. Through the MAE and RMSE metrics under the TP value in Figure 8, it can be found that with the increase in the highest interaction order, the MAE does not improve obviously, but the RMSE has a trend of improvement, which shows that with the increase in the highest interaction order in the local features, the prediction accuracy has not improved, but the prediction performance has become more stable. Similarly, since the increase in the highest interaction order will increase the running time of the model, this paper proposes to set the maximum interaction order to 2, while ensuring a certain stability and accuracy, and the running time of the model can be applied to many practical situations.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Approach**} & **Density = 0.05** & **Density = 0.10** & **Density = 0.15** & **Density = 0.20** & **Density = 0.25** & **Density = 0.30** & **Average** \\  & **MAE** & **MAE** & **MAE** & **MAE** & **MAE** & **MAE** & **MAE** \\ \hline LDIF-2 & 17.04 & 15.67 & 15.37 & 15.13 & 15.03 & 14.73 & **15.49** \\ \hline LDIF-3 & 17.29 & 15.82 & 15.42 & 15.15 & 14.98 & 14.71 & **15.56** \\ \hline LDIF-4 & 17.33 & 15.92 & 15.48 & 15.12 & 15.07 & 14.77 & **15.62** \\ \hline LDIF-5 & 17.63 & 15.99 & 15.51 & 15.15 & 14.98 & 14.78 & **15.67** \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparison of TP prediction performance in different highest orders on MAE.  
Figure 7: Comparison in different word-embedding dimensions. (**a**,**b**) represent the prediction performance of different word-embedding dimensions on MAE and RMSE metrics in TP, respectively.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Approach**} & **Density = 0.05** & **Density = 0.10** & **Density = 0.15** & **Density = 0.20** & **Density = 0.25** & **Density = 0.30** & **Average** \\  & **RMSE** & **RMSE** & **RMSE** & **RMSE** & **RMSE** & **RMSE** & **RMSE** \\ \hline LDIF-2 & 50.65 & 48.81 & 49.58 & 49.58 & 49.76 & 49.92 & **49.72** \\ \hline LDIF-3 & 51.14 & 48.53 & 48.84 & 49.70 & 48.71 & 48.70 & **49.26** \\ \hline LDIF-4 & 51.36 & 48.57 & 48.49 & 48.34 & 48.78 & 48.72 & **49.04** \\ \hline LDIF-5 & 51.29 & 48.62 & 48.91 & 48.50 & 48.47 & 48.80 & **49.10** \\ \hline \hline \end{tabular}
\end{table}
Table 9: Comparison of TP prediction performance in different highest orders on RMSE.