# Location-Aware Deep Interaction Forest for Web Service QoS Prediction
## 4 Experiment
### Experimental Results and Analysis
\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 & 2.221 & 3.337 & 1.959 & 3.058 & 1.581 & 2.599 & 1.691 & 2.558 & 1.709 & 1.846 & 1.633 & 2.019 \\ \hline IPCC & 3.539 & 4.971 & 2.846 & 4.216 & 2.083 & 3.372 & 1.618 & 2.806 & 1.438 & 2.588 & 1.352 & 2.473 \\ \hline UIPCC & 2.879 & 4.153 & 2.402 & 3.637 & 1.831 & 2.985 & 1.654 & 2.682 & 1.573 & 2.216 & 1.492 & 2.246 \\ \hline PMF & 3.557 & 4.990 & 3.555 & 4.989 & 3.555 & 4.99 & 3.555 & 4.991 & 3.558 & 4.994 & 3.558 & 4.990 \\ \hline DeepFM & 1.385 & 2.066 & 1.428 & 1.948 & 0.894 & 1.590 & 1.058 & 1.664 & 0.874 & 1.499 & 0.710 & 1.396 \\ \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 WDL & 1.382 & 2.128 & 1.013 & 1.724 & 1.362 & 1.944 & 0.818 & 1.513 & 0.907 & 1.607 & 0.939 & 1.536 \\ \hline DCN-V2 & 0.980 & 1.660 & 0.490 & 1.381 & 0.433 & 1.362 & 0.381 & 1.390 & 0.421 & 1.382 & 0.410 & 1.373 \\ \hline LDIF & **0.403** & **1.305** & **0.402** & **1.303** & **0.303** & **1.301** & **1.300** & **0.300** & **1.300** & **0.300** & **1.300** \\ \hline
**Gains** & **58.8\%** & **21.4\%** & **18.0\%** & **5.6\%** & **30.0\%** & **4.8\%** & **21.0\%** & **6.5\%** & **28.7\%** & **6.0\%** & **26.8\%** & **5.3\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of RT prediction performance. will be greatly reduced. At the same time, since the number of services in the dataset is much larger than the number of users, the running time of IPCC will be greatly increased compared with UPCC. The above shows that the memory-based collaborative filtering approach is greatly affected by the size of data sparsity and the size of the data volume. Surprisingly, the PMF has the lowest prediction performance among the two indicators. The main reason is that when the data sparsity is too small and the data volume is too large, the low-dimensional matrix decomposed by the scoring matrix cannot finally converge. This leads to an increase in the error, and it also shows that the model-based collaborative filtering approach does not perform well when the data sparsity is too small. The following DeepFM, DCN, and WDL combine the deep neural network, relatively fully utilizing the feature of the data and the influence of the feature interaction information from high- to low-order, so the prediction performance on the two QoS indicators of TP and RP is better than those traditional models. However, due to the fact that sufficient training of deep neural networks requires a sufficient amount of data, the prediction performance is not optimal in sparse data, while LDIF uses the multi-size of the local feature scan mechanism in the deep forest to effectively alleviate the problem of sparse data, and special interactive computing improves its ability to represent different-order feature interaction information. Thereby, especially in the case of more sparse data, its prediction performance is significantly better than other methods. Looking at the data in Tables 2 and 3 from the perspective of horizontal comparison, it can be observed that when the matrix density becomes smaller, the MAE and RMSE will still increase regardless of the approach. This phenomenon shows that although data sparsity will have different influences on various web service recommendation methods, its influence cannot be eliminated. This paper will evaluate the impact of data sparsity on LDIF models in detail in Section 4.5.