# Multipointer Coattention Recommendation with Gated Neural Fusion between ID Embedding and Reviews
## 4 Experimental Evaluation
### Parameter Sensitivity Analysis (Q2)  
Performance of rating prediction varies with model parameters. In this section, we adopt the control variables approach to explore the effect of three hyperparameters (number of hidden factors, dropout rate, and number of pointers) on rating prediction. Based on the previous work, we determine the range of values for the different parameters. In the process of exploring the effect of the number of hidden factors on the performance of each model, we found that the MSE values of MPCAR were much smaller than those of the comparison models. This also leads to the fact that we cannot clearly observe the variation of each model performance with the number of hidden factors in the plotted graphs. Therefore, we only show the effect of different numbers of hidden factors on the performance of the proposed MPCAR model, as shown in Figure 2. For the deep learning-based models (D-ATT, DeepCoNN, NARRE, MPCN, and MPCAR), we explored the effect of different dropout rates on the performance of each model, as shown in Figure 3. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & & & & & & **Our** & **Improvement** \\  & & & & & & & **Approach** & **(\%)** \\ \hline
**Metrics** & **Datasets** & **MF [4]** & **D-ATT [32]** & **DeepCoNN [5]** & **NARRE [6]** & **MPCN [7]** & **MPCAR** & \(\Delta_{\text{MN}}\) \\ \hline \multirow{6}{*}{MSE} & Instant Video & 2.769 & 1.004 & 1.285 & 1.096 & 0.997 & 0.906 & 10 \\  & Instruments & 6.720 & 0.964 & 1.483 & 0.951 & 0.923 & 0.858 & 7.6 \\  & Beauty & 1.950 & 1.409 & 1.453 & 1.396 & 1.387 & 1.270 & 9.2 \\  & Cellphone & 1.972 & 1.452 & 1.524 & 1.429 & 1.413 & 1.225 & 15.3 \\  & Gourmet Food & 1.537 & 1.143 & 1.199 & 1.106 & 1.125 & 1.054 & 6.7 \\  & Health & 1.882 & 1.269 & 1.299 & 1.246 & 1.238 & 1.077 & 14.9 \\  & Office Products & 1.143 & 0.805 & 0.909 & 0.817 & 0.779 & 0.682 & 14.2 \\  & Baby & 1.755 & 1.325 & 1.440 & 1.318 & 1.304 & 1.213 & 7.5 \\  & Digital Music & 1.956 & 1.000 & 1.202 & 0.965 & 0.970 & 0.857 & 13.1 \\  & Pet Supplies & 1.736 & 1.337 & 1.447 & 1.316 & 1.328 & 1.258 & 5.6 \\ \hline \multirow{6}{*}{MAE} & Instant Video & 1.467 & 0.770 & 0.839 & 0.768 & 0.781 & 0.715 & 9.2 \\  & Instruments & 2.38 & 0.689 & 0.751 & 0.718 & 0.697 & 0.670 & 4 \\  & Beauty & 1.381 & 0.837 & 0.922 & 0.828 & 0.894 & 0.813 & 10 \\  & Cellphone & 1.494 & 0.871 & 0.893 & 0.874 & 0.867 & 0.797 & 8.7 \\  & Gourmet Food & 1.206 & 0.731 & 0.718 & 0.731 & 0.704 & 0.693 & 1.6 \\  & Health & 1.27 & 0.725 & 0.739 & 0.727 & 0.712 & 0.703 & 1.3 \\  & Office Products & 0.996 & 0.754 & 0.707 & 0.720 & 0.670 & 0.615 & 8.9 \\  & Baby & 1.32 & 0.845 & 0.873 & 0.851 & 0.858 & 0.803 & 6.8 \\  & Digital Music & 1.204 & 0.697 & 0.722 & 0.686 & 0.729 & 0.660 & 10.4 \\  & Pet Supplies & 1.375 & 0.823 & 0.850 & 0.826 & 0.822 & 0.809 & 1.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance comparison (MSE and MAE) on 10 amazon subdatasets. \(\Delta_{\text{MN}}\) is the relative improvement of MPCAR over MPCN (%). The length of hidden vectors of users and items is the so-called number of hidden factors in the recommender system. The optimal result obtained by the model depends on whether the appropriate number of hidden factors is selected. By observing Figure 2, we can find that, as the number of hidden factors increases, MSE first decreases and then increases, reaching the lowest when the number of hidden factors is 16. In addition, with the increase in the number of hidden factors, the proposed MPCAR model does not appear to be over-fitting (the slow growth rate of the MSE value indicates that the model is not overfitting). This fully demonstrates the effectiveness of using deep learning to build recommendation models. Deep learning enables the models to obtain better generalization performance because the models do not rely too much on some local features. To prevent the model from overfitting, we use the dropout method to address the phenomenon of overfitting.