## 5 Result Analysis
Regarding R\({}^{2}\), as shown in Figure 9, the psychographic variables in Beauty, and Toys and Games obtain a significantly greater R\({}^{2}\) than that of Office Products (\(p\)-value \(<\) 0.05). In terms of the regression method, as shown in Figures 10 and 11, DNN is significantly better than the other three methods in terms of both RMSE and R\({}^{2}\). The performance of methods tends to follow a descending order from DNN to RF, LR, and SVR in the performance of preference prediction. Figure 8: Multiple comparisons between all pairs (Tukey) in product categories for psychographic variable segmentation-based rating prediction (RMSE). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ps1–Ps2 (Importance Ranking)** & **Diff** & **Lwr** & **Upr** & **P.adj** \\ \hline Pow–Conf & \(-6.5833\) & \(-11.4494\) & \(-1.7172\) & 0.0005 \\ Trad–Pow & \(6.4500\) & \(1.5839\) & \(11.3161\) & 0.0007 \\ S-D–Pow & \(6.7891\) & \(1.5331\) & \(12.0451\) & 0.0012 \\ Pow–Benev & \(-7.4661\) & \(-13.4258\) & \(-1.5064\) & 0.0021 \\ Univ–Benev & \(7.1006\) & \(1.1408\) & \(13.0603\) & 0.0049 \\ Sec–Benev & \(7.0118\) & \(0.7297\) & \(13.2940\) & 0.0131 \\ Univ–Stim & \(5.0000\) & \(0.1339\) & \(9.8661\) & 0.0371 \\ Univ–Achiev & \(5.0000\) & \(0.1339\) & \(9.8661\) & 0.0371 \\ \hline \hline \multicolumn{5}{c}{**Subdimensions of BFF**} \\ \hline Openness–Conscientiousness & \(6.7000\) & \(1.8339\) & \(11.5661\) & 0.0003 \\ Openness–Neuroticism & \(6.0498\) & \(0.7938\) & \(11.3058\) & 0.0084 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: _Cont._Figure 11: Multiple comparisons between all pairs (Tukey) in regression methods for psychographic segmentation-based rating explanation (R\({}^{2}\)). Figure 10: Multiple comparisons between all pairs (Tukey) in regression methods for psychographic segmentation-based rating prediction (RMSE). Figure 9: Multiple comparisons between all pairs (Tukey) in product categories for psychographic segmentation-based rating explaining (R\({}^{2}\)). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{6}{c}{**Tukey’s Test for Product Category**} \\ \hline
**Evaluations** & **g1** & **g2** & **Lower** & **Meandiff (g2–g1)** & **Upper** & **Reject (\(p\) Value \(<\) 0.05)** \\ \hline RMSE & Beauty & Digital Music & 0.057 & 0.119 & 0.181 & True \\ RMSE & Beauty & Grocery and Gourmet Food & 0.017 & 0.079 & 0.142 & True \\ RMSE & Beauty & Office Products & \(-\)0.130 & \(-\)0.067 & \(-\)0.004 & True \\ RMSE & Digital Music & Office Products & \(-\)0.248 & \(-\)0.186 & \(-\)0.124 & True \\ RMSE & Digital Music & Toys and Games & \(-\)0.202 & \(-\)0.141 & \(-\)0.079 & True \\ RMSE & Grocery and Gourmet Food & Office Products & \(-\)0.208 & \(-\)0.146 & \(-\)0.084 & True \\ RMSE & Grocery and Gourmet Food & Toys and Games & \(-\)0.162 & \(-\)0.101 & \(-\)0.040 & True \\ R\({}^{2}\) & Beauty & Office Products & \(-\)0.172 & \(-\)0.095 & \(-\)0.017 & True \\ R\({}^{2}\) & Office Products & Toys and Games & 0.004 & 0.081 & 0.157 & True \\ \hline \multicolumn{6}{c}{**Tukey’s Test for Regression Methods**} \\ \hline RMSE & LR & NN & \(-\)0.199 & \(-\)0.146 & \(-\)0.094 & True \\ RMSE & NN & RF & 0.088 & 0.140 & 0.192 & True \\ RMSE & NN & SVR & 0.130 & 0.182 & 0.235 & True \\ R\({}^{2}\) & LR & NN & 0.036 & 0.094 & 0.151 & True \\ R\({}^{2}\) & LR & SVR & \(-\)0.135 & \(-\)0.078 & \(-\)0.022 & True \\ R\({}^{2}\) & NN & RF & \(-\)0.140 & \(-\)0.082 & \(-\)0.025 & True \\ R\({}^{2}\) & NN & SVR & \(-\)0.229 & \(-\)0.172 & \(-\)0.115 & True \\ R\({}^{2}\) & RF & SVR & \(-\)0.146 & \(-\)0.090 & \(-\)0.034 & True \\ \hline \hline \end{tabular}
\end{table} 
Table 10: [PERSON]’s range test for product category and regression methods.