# Two-Layer Matrix Factorization and Multi-Layer Perceptron for Online Service Recommendation
## 4 Experiments
### Analysis of the Performance in Small Data Volumes  
The introduction of matrix factorization can provide linear capture capability to the model. In order to investigate whether matrix factorization can help to improve the performance of the model in scenarios with small data volumes, we used the method of clipping on the basis of the two datasets, namely, MovieLens 20M and MovieLens Latest, to divide a number of small datasets. The database was cut according to the proportions of 25%, 37.5%, 50%, 62.5%, and 75%, and then training was performed on the datasets that have been cut. By varying the dataset size and task types, we simulated 10 different scenarios for each of the two datasets. For the ml-latest dataset with more low-frequency data, the results can reflect the impact of prediction accuracy under low-frequency interactions, and the experiments can fully test our model's mitigation effect on  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{5}{c}{**ml-20m**} \\ \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**Binary**} & \multicolumn{2}{c}{**Multiclass**} \\ \cline{2-5}  & **Accuracy** & **MSE Loss** & **Accuracy** & **CE Loss** \\ \hline \multirow{2}{*}{**FIXED**} & 72.13 & 0.1845 & 49.45 & 1.1517 \\  & 72.18 & 0.1836 & 49.85 & 1.1423 \\  & 72.27 & 0.1828 & 49.94 & 1.1399 \\  & 72.98 & 0.1785 & 50.98 & 1.1155 \\  & **73.37** & **0.1766** & **51.48** & **1.1101** \\ \hline \hline \multicolumn{5}{c}{**ml-latest**} \\ \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**Binary**} & \multicolumn{2}{c}{**Multiclass**} \\ \cline{2-5}  & **Accuracy** & **MSE Loss** & **Accuracy** & **CE Loss** \\ \hline \multirow{2}{*}{**FIXED**} & 72.13 & 0.1845 & 50.01 & 1.1414 \\  & 72.22 & 0.1834 & 50.46 & 1.1304 \\  & 72.36 & 0.1823 & 50.47 & 1.1311 \\  & 72.88 & 0.1790 & 51.11 & 1.1147 \\  & **73.26** & **0.1773** & **51.37** & **1.1072** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Overall performance comparison on baselines and TMMNN models.  
overfitting. The ml-20m dataset, which mainly contains medium- and high-frequency data, is used to check whether the model can maintain the prediction accuracy in general, except in the special case of low-frequency small datasets. In this subsection, the experiment only changes the size of the two datasets so as to study the effect of dataset size on the linear capture ability of our model.  
Figure 4 shows the accuracy of the ESAPN and TMMNN on various datasets and tasks; the red line represents ESAPN, and the blue line represents TMMNN. The abscissa shows the percentage of full training batches. The original data set is randomly cropped by a percentage to serve as the training set, and the interaction information of the same user is selected as the test set in the remaining data. Combining the two datasets and the two classification tasks yields four different situations, which are shown separately in four subplots. The following conclusions can be drawn from the figure:  
1. Our model outperforms the ESAPN on all datasets and all tasks, which shows that the addition of matrix factorization can indeed improve the effectiveness of the model.
2. The model shows more improvement in ml-20m than in ml-latest. Considering that the amount of data the former dataset has is much smaller than that of the latter, this phenomenon can also be interpreted as the matrix factorization improving the model more significantly in a small amount of data.
3. It can be seen from the figure that when the size of the training dataset gradually increases, the performance of the TMMNN does not lag behind. This can be explained by the fact that as the amount of data increases, the multi-layer perceptron is gradually affected by the sufficient training, so the accuracy of the model can be guaranteed to continue to increase.