# Location-Aware Deep Interaction Forest for Web Service QoS Prediction
## 4 Experiment
### Experimental Results and Analysis  
In this experiment, the MAE and RMSE of nine methods are compared under six different matrix densities (5%, 10%, 15%, 20%, 25%, and 30%). In the UPCC, IPCC, and UIPCC methods, we set the maximum similarity neighborhood number k to 10; in the PMF method, the L1 regularity coefficient is set as 0.01 and the dimension of the latent vector is 10; the DeepFM, DCN, WDL, and DCN-V2 methods are implemented through the deepctr library; in the LDIF method, we set the number of decision trees to 100 and the max depth to 10. Each experiment is randomly divided into a training set and a test set, and a total of 10 replicates are performed. The average of all experimental results is taken as the final prediction result. Tables 2 and 3 show the prediction performance of LDIF and various comparison methods under different data sparsities. It can be clearly seen that under the six different matrix densities, the MAE and RMSE of LDIF are smaller than other methods, indicating that the prediction accuracy of this approach is higher than the current state-of-the-art methods. As can be seen from the table, traditional memory-based collaborative filtering methods of UPCC, IPCC, and UIPCC do not perform well. They are greatly affected by the matrix density. When the matrix density is 5%, the performance of other matrix densities \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 UPCC & 27.26 & 63.80 & 24.87 & 59.87 & 23.97 & 58.59 & 23.63 & 58.01 & 23.44 & 57.71 & 22.97 & 57.30 \\ \hline IPCC & 48.41 & 123.27 & 27.92 & 84.63 & 25.90 & 74.24 & 26.49 & 75.92 & 25.72 & 75.05 & 25.13 & 74.62 \\ \hline UIPCC & 37.82 & 93.53 & 26.40 & 72.25 & 24.93 & 66.42 & 25.06 & 66.96 & 24.58 & 66.37 & 24.05 & 65.96 \\ \hline PMF & 49.77 & 123.81 & 49.77 & 123.82 & 49.78 & 123.80 & 49.85 & 123.98 & 49.79 & 123.87 & 49.83 & 123.94 \\ \hline DeepFM & 25.37 & 57.85 & 21.83 & 55.16 & 18.16 & 48.17 & 18.85 & 50.66 & 18.39 & 47.82 & 18.04 & 48.64 \\ \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 WDL & 23.82 & 57.19 & 22.43 & 56.67 & 22.72 & 52.13 & 19.10 & 48.11 & 18.51 & 48.42 & 18.71 & 48.97 \\ \hline DCN-V2 & 22.53 & 49.02 & 20.91 & 46.47 & 17.64 & 41.78 & 17.23 & 41.94 & 15.95 & 41.50 & 16.28 & 41.31 \\ \hline LDIF & **17.20** & **48.14** & **15.81** & **45.50** & **15.40** & **40.84** & **15.10** & **39.65** & **14.91** & **40.71** & **14.71** & **39.70** \\ \hline
**Gains** & **23.7\%** & **1.8\%** & **24.4\%** & **2.1\%** & **12.7\%** & **2.2\%** & **12.4\%** & **5.5\%** & **6.5\%** & **1.9\%** & **9.6\%** & **3.9\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of TP prediction performance.