## 5 Result Analysis
Figures 6 and 7 show two intuitive expressions of these orders, respectively. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Importance** & **Importance** & **Feature** & **Product** & **Psychographic** & **Subdimension** & **Consumer** \\
**Ranking** & **Support** & **Source** & **Category** & **Psychographic** & **Subdimension** & **or Product** \\ \hline
8 & FALSE & pos & Digital Music & BFF & Openness & U \\ \hline
1 & TRUE & pos & Digital Music & BFF & Agreeableness & U \\ \hline
1 & TRUE & pos & Digital Music & BFF & Conscientiousness & U \\ \hline
6 & FALSE & pos & Digital Music & BFF & Neuroticism & I \\ \hline
5 & FALSE & pos & Digital Music & BFF & Extraversion & I \\ \hline
3 & FALSE & pos & Digital Music & BFF & Openness & I \\ \hline \(\ldots\) & \(\ldots\) & \(\ldots\) & Digital Music & \(\ldots\) & \(\ldots\) & \(\ldots\) \\ \hline
7 & FALSE & pos & Digital Music & SVS & Stim & U \\ \hline
26 & FALSE & pos & Digital Music & SVS & Conf & U \\ \hline
25 & FALSE & pos & Digital Music & SVS & Hed & U \\ \hline
5 & FALSE & pos & Digital Music & SVS & Trad & U \\ \hline
14 & FALSE & pos & Digital Music & SVS & Achiev & U \\ \hline
17 & FALSE & pos & Digital Music & SVS & Benev & U \\ \hline
4 & FALSE & pos & Digital Music & SVS & Pow & U \\ \hline
13 & FALSE & pos & Digital Music & SVS & Univ & U \\ \hline
16 & FALSE & pos & Toys and Games & SVS & S-D & I \\ \hline
29 & FALSE & pos & Toys and Games & BFF & Sec & I \\ \hline
1 & TRUE & pos & Toys and Games & BFF & Stim & I \\ \hline
9 & FALSE & pos & Toys and Games & BFF & Conf & I \\ \hline
24 & FALSE & pos & Toys and Games & BFF & Hed & I \\ \hline
19 & FALSE & pos & Toys and Games & SVS & Trad & I \\ \hline
22 & FALSE & pos & Toys and Games & SVS & Achiev & I \\ \hline
30 & FALSE & pos & Toys and Games & SVS & Benev & I \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Importance of different subdimensions of psychographic variable in predicting and explaining user preference. Figure 6: Difference between subdimensions of SVS in understanding consumer rating. Figure 7: Difference between subdimensions of BFF in understanding consumer rating. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ps1–Ps2 (Importance Ranking)** & **Diff** & **Lwr** & **Upr** & **P.adj** \\ \hline \multicolumn{5}{c}{**Subdimensions of SVS**} \\ \hline Pow–Achiev & \(-9.5667\) & \(-14.4328\) & \(-4.7006\) & \(0.0000\) \\ Pow-Hed & \(-10.4833\) & \(-15.3494\) & \(-5.6172\) & \(0.0000\) \\ Sec–Pow & \(14.4780\) & \(9.2220\) & \(19.7340\) & \(0.0000\) \\ Stim-Pow & \(9.5667\) & \(4.7006\) & \(14.4328\) & \(0.0000\) \\ Univ-Pow & \(14.5667\) & \(9.7006\) & \(19.4328\) & \(0.0000\) \\ Univ-Trad & \(8.1167\) & \(3.2506\) & \(12.9828\) & \(0.0000\) \\ Univ-Conf & \(7.9833\) & \(3.1172\) & \(12.8494\) & \(0.0000\) \\ Trad–Sec & \(-8.0280\) & \(-13.2840\) & \(-2.7720\) & \(0.0000\) \\ Sec–Conf & \(7.8946\) & \(2.6386\) & \(13.1506\) & \(0.0000\) \\ Univ-S-D & \(7.7776\) & \(2.5216\) & \(13.0336\) & \(0.0001\) \\ Sec–S-D & \(7.6889\) & \(2.0700\) & \(13.3078\) & \(0.0004\) \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Difference between subdimensions of psychographic in understanding user preference. Analysis of Product Category and Regression Methods in Predicting and Explaining E-Commerce User Preferences  
In this section, we analyze the internal differences in the new segment-wise preference regression methods and product categories that support the main research questions. Based on Table 4, we perform the [PERSON]'s range test on the preference prediction method and product categories; the details are shown in Table 10.  
Regarding RMSE, there were more significant differences in RMSE between different product categories compared to those in R\({}^{2}\), which is shown in Figure 8. The rating prediction performance of psychographics tends to follow a descending order from Office Products, which obtains the best predictive power, to Toys and Games, Beauty, Grocery and Gourmet Food, and Digital Music.