# An Improved Deep Belief Network Prediction Model Based on Knowledge Transfer
## 4 Experiments
### Rating Classification Prediction Based on User Data
#### 4.3.2 Predictive Performance Evaluation  
In this experiment, different configurations of the ML-20M data set were performed. First, 50,000 users were randomly selected, and 30,000 users were selected as the training set, and the remaining 20,000 users were selected as the test set. According to the number of training users, the training set is divided into the following three categories: MovieLens3w, MovieLens2w, Movielens1w. For each test user, the ratings in the range of 100 (200,300) are called Rating0-100, Rating100-200, and Rating200-300. Through these different configuration combinations, a total of 9 data sets are obtained: M3 wR0-100, M3 wR100-200, M3 wR200-300, M2 wR0-100, M2 wR100-200, M2 wR200-300, M1 wR0-100, M1 wR100-200, M1 wR200-300. Since the sparsity of the data set is an important factor that affects the experimental results, this paper  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Rating** & **Coding** & **Label** & **Rating** & **Coding** & **Label** \\ \hline
0.5 & 0000000001 & 0.5 sort & 3 & 0000100000 & 3 sort \\
1 & 0000000010 & 1 sort & 3.5 & 0001000000 & 3.5 sort \\
1.5 & 0000000100 & 1.5 sort & 4 & 0010000000 & 4 sort \\
2 & 0000001000 & 2 sort & 4.5 & 0100000000 & 4.5 sort \\
2.5 & 0000010000 & 2.5 sort & 5 & 1000000000 & 5 sort \\ \hline \hline \end{tabular}
\end{table}
Table 3: Rating scale.  
Figure 9: Comparison of actual ratings and predicted ratings.  
conducts experiments on data sets with different degrees of sparsity. This kind of method of configuring data sets is widely used in the field of collaborative filtering [23; 24]. The experimental results are shown in Table 4.  
It can be seen from Table 4 that the IDBN proposed in this paper has achieved better results under different sparseness data sets. Since most of the ML-20M data set is user statistical information and movie attribute information, the method in this paper is only based on predictions made for user rating information. It can be seen that the richer the user rating information is, the more the feature information obtained by the model is sufficient. The lower the RMSE value, the higher the predictive performance of the model.  
In order to further evaluate the predictive performance of IDBN, we compared IDBN with traditional recommendation algorithms. These algorithms include singular value decomposition (SVD), restricted boltzmann machine (RBM), k-nearest neighbor algorithm (KNN), and traditional DBN. Iterative training is carried out under the M3 wR200-300 data set, where the KNN method has nothing to do with the number of iterations, so the experimental results of KNN are constant. As the number of iterations of the SVD method increases, the RMSE curve changes significantly, and there is a trend of convergence when the iterations are about 130 times. The IDBN proposed in this paper has a tendency of convergence of about 80 iterations, and its RMSE reaches 70.45%. The experimental results show that IDBN has a faster convergence speed and higher prediction classification accuracy. The experimental results are shown in Figure 10.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**User Number** & **Methods** & **Rating0-100** & **Rating100-200** & **Rating200-300** \\ \hline \multirow{3}{*}{\(1\)w} & DBN & 0.7635 & 0.7542 & 0.7354 \\  & SVD & 0.8042 & 0.7824 & 0.7642 \\  & IDBN & **0.7454** & **0.7334** & **0.7132** \\  & DBN & 0.7653 & 0.7435 & 0.7311 \\
2w & SVD & 0.7834 & 0.7735 & 0.7565 \\  & IDBN & **0.7135** & **0.7049** & **0.7045** \\  & DBN & 0.7632 & 0.7443 & 0.7256 \\
3w & SVD & 0.7737 & 0.7642 & 0.7586 \\  & IDBN & **0.7143** & **0.7094** & **0.7022** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Route mean square error (RMSE) of each recommendation algorithm under different sparseness data.  
Figure 10: The accuracy of the recommendation algorithm.