# Comparative Analysis of SAAS Model and NPC Integration for Enhancing VR Shopping Experiences
## 3 Methodology
### Integration with the Shopping Dataset  
In this section, we describe how we integrated the shopping datasets collected from open sources (as outlined in Section 1) with the neutral language models (as detailed in Section 2). The objective is to develop a robust suggestion system that leverages the strengths of various deep learning models to provide personalized shopping recommendations.  
Data Preparation and Preprocessing:  
* Data Aggregation: Combine the various datasets (Amazon Product Dataset, Amazon Customer Reviews, Retail Dataset, etc.) into a single unified dataset.  
Figure 5: Flowchart of the SAAS Model.  
* Ensure consistency in the format by aligning all datasets to a common schema with fields: User ID, Review, Product ID, Product Type, Action Type, and Rating.
* Data Cleaning: Remove duplicate entries to avoid bias in the model training process.
* Handle missing values by either imputing with average values or removing incomplete records depending on the extent of missingness.
* Normalization:
* Normalize text data by converting to lowercase, removing stop words, and stemming/lemmatization.
* Normalize numerical data (ratings, etc.) to a common scale to ensure uniformity across the dataset.  
Model Training and Testing: The unified dataset is used to train and test the following deep learning models: GPT-3, BERT, T5, and SAAS.  
Each model is fine-tuned using the unified shopping dataset to optimize it for the task of generating personalized shopping suggestions. The training and testing processes for each model are described below.  
1. GPT-3 Training and Testing:
* Training: Fine-tune GPT-3 on the unified dataset using transfer learning techniques.
* The model learns the context of shopping behaviors and preferences from the review texts and associated metadata.
* Testing: Evaluate GPT-3's performance on a separate validation set.
* Measure metrics such as accuracy, precision, sensitivity, specificity, and F1 score to assess model performance.
2. BERT Training and Testing:
* Training: Use the pre-trained BERT model and further train it on the unified dataset.
* Focus on fine-tuning BERT for tasks such as text classification and sentiment analysis.
* Testing: Test BERT's performance on the validation set.
* Evaluate using the same metrics as GPT-3 to ensure a fair comparison.
3. T5 Training and Testing:
* Training: Fine-tune T5 for text-to-text tasks using the unified dataset.
* Train the model to generate suggestions based on the context of the review texts.
* Testing: Validate T5's performance on a separate test set.
* Use accuracy, precision, sensitivity, specificity, and F1 score as evaluation metrics.
4. SAAS (Shopping Assistance Automatic Suggestion) Training and Testing:
* Training: Develop and train the SAAS model using a combination of the techniques used in GPT-3, BERT, and T5.
* Focus on creating a model that leverages the strengths of all three to provide highly accurate and context-aware suggestions.
* Testing: Test the SAAS model on the validation set.
* Evaluate using comprehensive metrics to compare its performance against other models.  
Integration Process:
* The integration of the dataset with the models involves several key steps:
* Preprocessing Pipeline: Create a preprocessing pipeline that standardizes the input data for all models.
* Training Pipeline: Set up a training pipeline that ensures each model is trained under similar conditions for a fair performance comparison.
* Evaluation Pipeline: Develop an evaluation pipeline that applies the same metrics across all models to objectively assess their effectiveness.