## 5 Results
### Influence of the CORES Modules on System Performance  
To evaluate the impact of the CORES modules including conversation state management (CSM), recommendation engine, response generator, VR loader, and registries and profiles on overall system performance and user decision making, we used K-fold cross-validation (K = 5) to assess key performance metrics, including accuracy, precision, recall, and F1-score. The results are illustrated in Figure 14, providing a comprehensive analysis of each module's contribution to enhancing system effectiveness. Furthermore, to validate the statistical significance of these modules' impact, we applied the Friedman test to analyze the effect of each module. This analysis was supported by calculating chi-square (\(\chi^{2}\)) and \(p\)-value, based on the same set of subjects to test the following hypotheses:  
**H0:**: _the removal of the module does not significantly impact the system performance._
**H1:**: _the removal of the module significantly impacts the system performance._  
For the CSM module, the Friedman test exhibited statistically significant differences in the results (\(\chi^{2}\) = 18.24, \(p\) = 0.002). Cross-validation revealed that the inclusion of CSM had a substantial impact on CORES' recommendations, with accuracy (94.2% \(\pm\) 1.3%), precision (72.1% \(\pm\) 1.5%), recall (98.0% \(\pm\) 1.2%), and F1-score (83.4% \(\pm\) 1.7%) all being significantly affected. These results highlight the pivotal role of the CSM module in tracking users' states and interaction histories, ensuring more accurate context-aware recommendations. Regarding the recommendation engine, the Friedman test demonstrated highly statistically significance differences in the results (\(\chi^{2}\) = 22.35, \(p\) = 0.0005). Cross-validation revealed that the recommendation engine impacted the system performance, particularly in recall (91.6% \(\pm\) 2.3%) and F1-score (78.7% \(\pm\) 2.1%) compared with accuracy (92.3% \(\pm\) 1.5%) and precision (70.2% \(\pm\) 1.7%). This suggests that richer contextual information and social emotions improved CORES' precision, leading to more relevant recommendations. This \begin{table}
\begin{tabular}{l c c c} \hline
**Performance Metrics (\%)** & **Without Context** & **With Context and Preferences** & **With Context, Preferences, and Emotions** \\ \hline Accuracy & 94\% & 97\% & 97\% \\ Precision & 75\% & 75\% & 75\% \\ Recall & 60\% & 100\% & 100\% \\ F1-score & 66.7\% & 85.7\% & 85.7\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Performance evaluation of the recommendation system with and without context and emotion consideration selection (preference color >> thickness >> eco-friendly). also supports the concept that users benefited from context-aware social feedback, thus increasing their satisfaction. Furthermore, cross-validation also revealed a significant impact on the system's performance due to the response generator, with accuracy (90.1% \(\pm\) 1.6%), precision (67.4% \(\pm\) 2.1%), recall (89.7% \(\pm\) 2.0%), and F1-score (75.5%\(\pm\) 2.5%) all being affected. Furthermore, the Friedman test also showed statistically significant differences in the results (\(\chi^{2}\) = 19.50, \(p\) = 0.001). These results suggest that semantic analysis and intent extraction enhanced the credibility of explanations, making recommendations clearer and more transparent. Participants found that categorizing explanations by dimension improved clarity and user-friendliness. For the VR loader, cross-validation demonstrated a significant statistical impact on the system performance, with accuracy (88.9% \(\pm\) 2.1%), precision (66.8% \(\pm\) 2.5%), recall (86.5% \(\pm\) 3.1%), and F1-score (72.7% \(\pm\) 2.8%). The Friedman test revealed statistically significant differences in the results (\(\chi^{2}\) = 16.78, \(p\) = 0.004). These results show that VR integration enabled CORES to provide more precise item recommendations based on contextual information.