## 5 Result Analysis
Compared with BFF, the SVS is slightly more powerful in predicting user ratings (RMSE) for Beauty and Digital Music, whereas there is no difference between them for Grocery and Gourmet Food, and Office Products. However, the SVS was useless in predicting user ratings (RMSE) for Toys and Games, whereas BFF improved the predictive power of user ratings. 2.3 Q3: Analysis of the Adaptability of Psychographic Measurement in Predicting and Explaining E-Commerce User Preferences  
In Section 5.2.2, we studied the predictive and explanatory power of psychographic segmentation across different product categories. However, it remains unclear whether all subdivision dimensions of psychographic variables help understand user preferences across different e-commerce product categories. Table 8 shows part of the results of different subdimensions' psychographic variables in predicting and explaining user preferences using the RFE-SVM approach. In Table 8, feature ranking corresponds to the ranking position of the \(i_{\text{th}}\) psychographic subdimension, where the selected, i.e., estimated best dimensions are assigned rank 1; \"importance support\" represents these selected dimensions (with value TRUE); \"feature source\" represents whether a dimension is positively or negatively related to the SVS or BFF; \"product category\" contains five products from Table 8; \"psychographic\" contains three psychographic segmentations including the SVS, BFF, and random segmentation; and \"subdimension\" represents the subdimensions of positive and negative SVS and BFF. There are 810 rows in Table 8, and 150 rows with psychographic subdimensions that support our rating prediction models. We group the importance ranking in the 150 rows by product category and plot the total rankings for different subdimensions of the SVS and BFF. Only approximately \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Evaluations** & **G1** & **G2** & **Lower** & **Meandiff (G2-G1)** & **Upper** & **Reject (\textgreater{p} \textless{} 0.05)** \\ \hline RMSE & BFF & Random & \(-\)0.0145 & 0.0393 & 0.0932 & False \\ \hline RMSE & BFF & SVS & \(-\)0.052 & \(-\)0.0037 & 0.0445 & False \\ \hline RMSE & Random & SVS & \(-\)0.0964 & \(-\)0.0431 & 0.0102 & False \\ \hline R\({}^{2}\) & BFF & Random & \(-\)0.158 & \(-\)0.1091 & \(-\)0.0602 & True \\ \hline R\({}^{2}\) & BFF & SVS & \(-\)0.0196 & 0.0242 & 0.0681 & False \\ \hline R\({}^{2}\) & Random & SVS & 0.0849 & 0.1333 & 0.1817 & True \\ \hline \hline \end{tabular}
\end{table} 
Table 7: [PERSON]’s test for different psychographic variables. Figure 5: Mean values of RMSE and R\({}^{2}\) about three segmentation variables across different product Categories. (**a**) Mean values of RMSE; (**b**) Mean values of R\({}^{2}\). 18.5% of the subdimensions of the psychographic measurements were effective in understanding specific e-commerce preferences. We can see that within Table 8, with the exception of Sec and Univ, all subdimensions of the SVS and BFF are useful. However, in different product categories, there are more subdimensions that are not effective in understanding consumer preferences, including Benev, Hed, Trad, Openness, and Extraversion in Beauty; Benev, Conf, and Openness in Office Products; Benev, Hed, Trad, Openness, and Extraversion in Toys and Games; Achiev, Hed, and Stim in Digital Music; and Achiev, Benev, Conf, and Openness in Grocery and Gourmet Food. We conduct [PERSON]'s range test based on the 150 rows with psychographic subdimensions supporting our regression models; Table 9 shows the results. There are 37 significant comparisons that indicate the relationship between subdimensions of the SVS and BFF. From Table 9, we can see that, in terms of the effectiveness of psychographic subdimensions in understanding consumer preferences, the SVS subdimensions tend to follow the descending order of Pow, Trad, Conf, SD, Benev, and Stim. For subdimensions of BFF, the descending order is Conscientiousness, Neuroticism, and Openness.