## 5 Results and Discussion
### Experimental Result and Analysis  
All models were trained five times to evaluate the four comparison models and the proposed model. The evaluation functions were RMSE and MAE; the experimental results are shown in Table 4.  
The best training results of each model were analyzed. The ALS model obtained RMSE of 0.9641 and MAE of 0.7307, better than the similar deep-learning model FAST AI Embedding Dot Bias. The SVD model obtained 0.8448 as RMSE  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Trial No.**} & \multicolumn{2}{c}{**ALS**} & \multicolumn{2}{c}{**SVD**} & \multicolumn{2}{c}{**Fast AI Embedding**} & \multirow{2}{*}{**SAR**} & \multicolumn{2}{c}{**Feature Extracted**} \\  & & & & \multicolumn{2}{c}{**Dot Bias**} & & & \multicolumn{2}{c}{**Deep Neural CF**} \\ \cline{2-11}  & **RMSE** & **MAE** & **RMSE** & **MAE** & **RMSE** & **MAE** & **RMSE** & **MAE** & **RMSE** & **MAE** \\ \hline
1 & 0.9641 & 0.7307 & 0.8560 & 0.6683 & 0.9711 & 0.7781 & 1.6783 & 1.4239 & 0.8424 & 0.6593 \\
2 & 0.9686 & 0.7325 & 0.8555 & 0.6684 & 0.9656 & 0.7722 & 1.6755 & 1.4190 & 0.8418 & 0.6587 \\
3 & 0.9651 & 0.7309 & 0.8548 & 0.6673 & 0.9652 & 0.7719 & 1.6683 & 1.4138 & 0.8429 & 0.6598 \\
4 & 0.9685 & 0.7340 & 0.8565 & 0.6689 & 0.9655 & 0.7722 & 1.6657 & 1.4119 & 0.8424 & 0.6594 \\
5 & 0.9683 & 0.7324 & 0.8558 & 0.6681 & 0.9653 & 0.7720 & 1.6682 & 1.4149 & 0.8428 & 0.6597 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance comparison of the models.  
Figure 3: Convergence plot.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Kind of Data** & **Search Space** \\ \hline Size of embedding in the input layer & 40 \\ Regularization method type in the embedding input layer & L1 \\ Number of 1D convolution layer filters of feature extraction layer & 108 \\ Size of 1D convolution layer kernel of feature extraction layer & 24 \\ Number of neurons in the first fully connected layer of feature extraction layer & 14 \\ Activation function type in the second fully connected layer & _SelLU_ \\ Number of neurons in the first fully connected layer of the multilayer perceptron & 60 \\ Number of neurons in the second fully connected layer of the multilayer perceptron & 216 \\ Number of neurons in the third fully connected layer of the multilayer perceptron & 258 \\ Number of neurons in the fourth fully connected layer of the multilayer perceptron & 210 \\ Type of loss function & MeanAbsoluteError \\ Value of learning rate & 0.000495 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Selected hyperparameters for the deep neural CF model with feature extraction.  
highest values among all models excluding the proposed model. The FAST AI Embedding Dot Bias model obtained 0.9652 and 0.7719 as RMSE and MAE values, respectively, and outperformed the SAR model but not the ALS and SVD models. The SAR model obtained 1.6657 as RMSE and 1.4119 as MAE, which was the worst performance among the models used for comparison. Finally, the proposed model obtained 0.8418 as RMSE and 0.6587 as MAE, which means it outperformed all the other models.  
The experimental results and analysis show that the proposed model, deep neural CF with feature extraction, obtained the lowest RMSE and MAE values. In particular, the proposed model performed better than the traditionally used matrix-factorization-based CF models. Therefore, it is possible to conclude that the proposed model best analyzed the relationship between user and book data.