## 4 Implementation and Experiment Results
### Parameter Analysis
#### 4.3.1 Dimension Reduction Using the DAE  
In this study, the DAE is used to extract the essential features from the textual vector. Two critical factors in the DAE, the number of nodes in the hidden layers and the number of nodes in the latent representation layer (i.e., **y**), are further studied, while other parameters are the same as those in Table 1. First, the number of nodes in the hidden layers is changed according to a predefined compression ratio where the compression ratio is defined as  
\[\text{compression ratio}=\frac{\text{number of nodes in the input layer}}{\text{number of nodes in the desired layer}} \tag{10}\]  
For example, if the compression ratio is 10, the number of nodes in the hidden layer will be 1644 (=16,444/10). In this experiment, the set of compression ratios (2, 2.5, 3.3, 5, 10, 12.5, 16.7, 25, 50, and 100) is conducted. Figure 3(a) shows the relationship between the validation loss and epoch for different compression ratios. It is observed that the lowest validation loss for most compression ratios occurred when the epoch is between 11 and \begin{table}
\begin{tabular}{c c} \hline \hline
**Ranking** & **Movie ID** \\ \hline
1 & 3582 \\
2 & 10,789 \\
3 & 5072 \\ \(\cdots\) & \(\cdots\) \\
49 & 587 \\
50 & 7701 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Top 50 recommended movies for User ID 6. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**User ID** & **Movie ID** & **549** & **617** & **684** & **990** & **1007** \\ \hline
1 & - & 3 & 4 & 5 & 4 \\
2 & 4 & 5 & 4 & 3 & 4 \\
3 & 4.50 & 4.50 & - & - & 4 \\
4 & 3.50 & - & - & - & - \\ \(\cdots\) & \(\cdots\) & \(\cdots\) & \(\cdots\) & \(\cdots\) & \(\cdots\) \\
87,120 & 4 & 5 & - & - & 4 \\
87,121 & - & 5 & - & - & 3 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The predicted ratings for five example CS movies. \begin{table}
\begin{tabular}{c c} \hline \hline
**Name** & **Value** \\ \hline Number of nodes for users and movies in the input layer & (87,122, 12,474) \\ Number of nodes for users and movies in the embedding layer & (32, 32) \\ Number of hidden layers in the MLP & 2 \\ Number of nodes in the hidden layers of the MLP & (50, 10) \\ Batch size & 200 \\ Loss function & Mean squared error \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The structure and parameter settings for the NNCF predictor. 16. Besides, when the compression ratio is 10 (at epoch 12) and 16.7 (at epoch 13), the validation loss is minimum. Figure 3(b) shows the training time of the DAE when different compression ratios are applied. The training time surges when the compression ratio is small. Therefore, considering the computational efficiency and model accuracy, the number of nodes in the hidden layer is suggested as 987 (=16,444/16.7). Second, the number of nodes in the latent representation layer is also changed according to a set of compression ratios. Figure 4(a) shows the relationship between the validation loss and epoch for compression ratios 100, 125, 166.6, 250, 500, and 1000. At the first several epochs, the validation loss is significant for high compression ratio cases. After that, the loss decreases slowly for all compression ratios. However, the computational time for evaluating item similarity in Equation (4) will be much longer if a small compression ratio is applied. Figure 4(b) illustrates the computational time for item similarity evaluation for several compression ratios. Note that compression ratio 1 indicates no DAE feature reduction function is applied. When no feature reduction is used, the time for item similarity evaluation is 11.49 seconds (i.e., the vector's dimension is 16,444). Significantly, it takes only 2.87 seconds to complete the item similarity calculation if the dimension of the vector is reduced to 164 (i.e., the compression ratio is 100). Therefore, considering the computational efficiency and model accuracy, the number of nodes in the latent representation layer is suggested as 164.