# Location-Aware Deep Interaction Forest for Web Service QoS Prediction
## 4 Experiment
### The Impact of Word-Embedding Dimensions  
This experiment evaluates the effect of a word-embedding dimension on prediction performance by comparing this algorithm prediction performance under different word-embedding dimensions. We separately set the embedding dimension as 50, 200, and 300 dimensions to simulate most situations in reality. Since the effect of expressing the prediction performance of the QoS value is the same whether it is TP or RP, this experiment only uses TP as the target QoS value. The same as the six data sparsities taken in the above experiments, Table 6 uses the MAE metric, and Table 7 uses the RMSE metric, and the average values under the six data sparsities are calculated. Experiments show in Figure 7 that with the increase in the word-embedding dimension, MAE and RMSE are relatively stable and the relative improvement effect is not significant. Besides that, we found that when the word-embedding dimension increases, it increases the time overhead of data preprocessing, which will increase the overall running time of the experiment. Therefore, this paper suggests that it is more reasonable to set the word-embedding dimension as 50. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Approach** & \begin{tabular}{c} **Density = 0.05** \\ MAE \\ \end{tabular} & \begin{tabular}{c} **Density = 0.10** \\ RMSE \\ \end{tabular} & \begin{tabular}{c} **Density = 0.10** \\ MAE \\ \end{tabular} & \begin{tabular}{c} **Density = 0.15** \\ **RMSE** \\ \end{tabular} & \begin{tabular}{c} **Density = 0.20** \\ MAE \\ \end{tabular} & \begin{tabular}{c} **Density = 0.25** \\ **RMSE** \\ \end{tabular} & \
\begin{tabular}{c} **Density = 0.30** \\ **MAE** \\ \end{tabular} &
\begin{tabular}{c} **Density = 0.30** \\ **RMSE** \\ \end{tabular} \\ \hline DCN & 24.64 & 55.44 & 22.37 & 51.82 & 20.57 & 51.03 & 19.41 & 50.72 & 19.09 & **48.96** & 18.94 & **49.21** \\ \hline SIS + DCN & **17.63** & **51.01** & **17.41** & **49.27** & **15.75** & **48.70** & **15.39** & **48.29** & **15.64** & 49.67 & **15.31** & 49.55 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of TP prediction performance between DCN and SIS + DCN. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Approach** & \begin{tabular}{c} **Density = 0.05** \\ MAE \\ \end{tabular} & \begin{tabular}{c} **Density = 0.10** \\ RMSE \\ \end{tabular} & \begin{tabular}{c} **Density = 0.10** \\ **MAE** \\ \end{tabular} & \begin{tabular}{c} **Density = 0.15** \\ **RMSE** \\ \end{tabular} & \begin{tabular}{c} **Density = 0.20** \\ **MAE** \\ \end{tabular} & \begin{tabular}{c} **Density = 0.25** \\ **RMSE** \\ \end{tabular} & \
\begin{tabular}{c} **Density = 0.30** \\ **MAE** \\ \end{tabular} &
\begin{tabular}{c} **Density = 0.30** \\ **RMSE** \\ \end{tabular} \\ \hline DCN & 2.639 & 4.004 & 1.816 & 2.719 & 1.565 & 2.323 & 1.462 & 2.108 & 1.092 & 1.709 & 1.012 & 1.626 \\ \hline SIS + DCN & **0.498** & **1.375** & **0.438** & **1.376** & **0.405** & **1.379** & **0.385** & **1.451** & **0.356** & **1.470** & **0.335** & **1.479** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of RT prediction performance between DCN and SIS + DCN. \begin{table}
\begin{tabular}{l 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-50 & 17.29 & 15.82 & 15.42 & 15.15 & 14.98 & 14.71 & **15.56** \\ \hline LDIF-200 & 17.23 & 15.80 & 15.58 & 15.18 & 14.98 & 14.78 & **15.59** \\ \hline LDIF-300 & 17.21 & 15.85 & 15.54 & 15.19 & 14.97 & 14.71 & **15.58** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of TP prediction performance in different word-embedding dimensions on MAE.