## 5 Result Analysis  
Analysis of the Significance of Different Experimental Settings in Predicting and Explaining E-Commerce User Preferences  
We evaluate the predictive and explanatory power of user preference prediction based on LR, support vector machine (SVM), random forest (RF), and DNN, integrating consumer psychographic segmentation across different Amazon product categories. Table 4 shows part of the results of RMSE and R\({}^{2}\) under different experimental settings for the user preference prediction process. The nine columns are \"ProductCatrgory\", \"ClusterId\", \"SampleNum\", \"Psychographic Variable\", \"Clustering or Not\", \"RegressionMethods\", \"RMSE\", \"R2\" and \"ReviewLen\". The \"ProductCatrgory\" column contains five Amazon product categories from Table 3; \"ClusterId\" is the tag for each segment; \"SampleNum \" is the number of Amazon consumer ratings and reviews; \"Psychographic Variables \"; \"Psychographic Variables\" is the psychographic variable that we use to perform consumer segmentation; \"Clustering or Not\" is the experiment setting of applied DBSCAN clustering that determines whether consumer segmentation is performed; \"RegressionMethods\" is the regression model used in preference prediction; and \"RMSE\" and \"R2\" are the RMSE and R\({}^{2}\), respectively, for regression model evaluation; \"ReviewLen\" is the average amount of words in review for each dataset. We perform analysis of variance (ANOVA) on the results to test whether different experimental settings (product category, predictive algorithms, psychographic variables, and clustering or not) have an influence on the performance (R\({}^{2}\) and RMSE) of preference prediction. Figure 4: Deep neural network training and testing with different epochs. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Product Category** & **ClusterId** & **SampleNum** & **Psychographic Variables** & **Clustering or Not** & **RegressionMethods** & **RMSE** & **R2** & **ReviewLen** \\ \hline Tors and Games & Random & 9577 & Random & Random & RF & 0.9544 & \(-\)0.0462 & 105,3555 \\ \hline Tors and Games & Random & 9577 & Random & Random & RF & 0.9532 & \(-\)0.0434 & 105,3555 \\ \hline Tors and Games & Random & 9577 & Random & Random & LR & 0.9362 & \(-\)0.0065 & 105,3555 \\ \hline Tors and Games & Random & 9577 & Random & Random & LR & 0.9351 & \(-\)0.0042 & 105,3555 \\ \hline Tors and Games & All & 9577 & BFF & Not Clustered by DBSCAN & SVR & 0.9091 & 0.0509 & 105,3555 \\ \hline Tors and Games & All & 9577 & BFF & Not Clustered by DBSCAN & LR & 0.9084 & 0.0524 & 105,3555 \\ \hline Tors and Games & All & 9577 & SVS & Not Clustered by DBSCAN & SVR & 0.9067 & 0.0558 & 105,3555 \\ \hline Tors and Games & All & 9577 & SVS & Not Clustered by DBSCAN & LR & 0.9007 & 0.0684 & 105,3555 \\ \hline Tors and Games & Cluster\_2 & 2044 & SVS & Clustered by DBSCAN & NN & 1.1217 & 0.0311 & 105,3555 \\ \hline Tors and Games & Cluster\_2 & 2044 & SVS & Clustered by DBSCAN & RF & 0.9350 & 0.0746 & 105,3555 \\ \hline Digital Music & Cluster\_1 & 3920 & SVS & Clustered by DBSCAN & SVR & 1.1081 & \(-\)0.0632 & 205,5029 \\ \hline Digital Music & Cluster\_1 & 3920 & SVS & Clustered by DBSCAN & RF & 1.0575 & 0.0317 & 205,5029 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Evaluation results of user preference prediction under different experimental settings (partial part of). Table 5 shows the ANOVA results which demonstrate that the product category, clustering method, and regression methods have a significant effect on both R\({}^{2}\) and RMSE (\(p\)-value \(<\) 0.000). For the segmentation variables that we use (SVS, BFF, and random segmentation), there are significant differences between the three groups (\(p\)-value \(<\) 0.05) for R\({}^{2}\), whereas the difference is not significant for RMSE.