# Comparative Analysis of SAAS Model and NPC Integration for Enhancing VR Shopping Experiences
## 4 Results
### Suggestion System Performance  
The core of our suggestion system is to provide personalized recommendations based on user-selected items, leveraging the user's previous purchases or views. The SAAS model demonstrated superior performance in terms of both accuracy and speed, making it the most effective model for our recommendation system.  
Example of Suggestions:  
To illustrate the practical application of our system, we present examples of suggestions generated by each model for a given user scenario. The user has previously purchased electronics and viewed several home goods items.  
Figure 6: Comparison of Model Performance Metrics.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Accuracy** & **Precision** & **Sensitivity** & **Specificity** & **F1 Score** &
\begin{tabular}{c} **Runtime** \\ **Performance** \\ \end{tabular} \\ \hline GPT-3 & 0.95 & 0.94 & 0.96 & 0.93 & 0.95 & 30 ms \\ \hline BERT & 0.92 & 0.91 & 0.93 & 0.9 & 0.92 & 25 ms \\ \hline T5 & 0.93 & 0.92 & 0.94 & 0.91 & 0.93 & 28 ms \\ \hline SAAS & 0.96 & 0.93 & 0.95 & 0.94 & 0.94 & 32 ms \\ \hline \hline \end{tabular}
\end{table}
Table 3: Model Performance Metrics.  
Example Scenario:  
User ID: A3R5 OBKS/OM2 IR  
Previous Purchases: Electronics (e.g., Portable Speaker)  
Recent Views: Home Goods (e.g., Smart Home Devices)  
Generated Suggestions:  
* GPT-3: \"Based on your recent purchases, you might like these new arrivals in electronics: Wireless Earbuds, Smartwatch.\"
* BERT: \"Customers who bought the Portable Speaker also bought these products: Bluetooth Headphones, Home Security Camera.\"
* T5: \"We noticed you were looking at Smart Home Devices. Here are some similar items you might like: Smart Light Bulbs, Voice-Activated Assistants.\"
* SAAS: \"You might be interested in these products based on your shopping history: Noise-Cancelling Headphones, Smart Thermostat, Portable Power Bank.\"  
The suggestion system's workflow is designed to provide customized shopping recommendations by leveraging the features of the SAAS (Shopping Assistance Automatic Suggestion) architecture, as depicted in Figure 7.  
The procedure entails four primary stages:  
* User Input: The procedure commences when the user chooses an item or peruses a product category on the shopping platform. The initial input for the suggestion system is recorded as this encounter.
* Data Processing: Upon receiving the user's input, the system retrieves the user's shopping history, which encompasses previous purchases, ratings, reviews, recent views, and interactions. The acquisition of this extensive data is crucial for comprehending the user's preferences and behavior. Subsequently, the data undergo preprocessing to guarantee uniformity and precision before being inputted into the model.
* Model Application: The SAAS model handles the data that have been retrieved and preprocessed. The SAAS model use a combination of the GPT-3, BERT, and T5 models to study the user's behavior and preferences. It then generates a personalized list of item suggestions. This step entails intricate calculations and the utilization of advanced machine learning methods to generate pertinent and precise recommendations.
* Output Generation: The generated suggestions are then presented to the user in a user-friendly format. This could be in the form of a list of recommended products or a carousel of items displayed on the shopping platform. The suggestions aim to enhance the user's shopping experience by offering items that are most relevant and appealing based on their past interactions and preferences.  
This workflow ensures that users receive personalized and contextually relevant shopping recommendations, thereby improving their overall shopping experience and satisfaction.