Comparative Analysis of SAAS Model and NPC Integration for Enhancing VR Shopping Experiences

2.1. AI NPCs in VR Shopping

2.2. Deep Learning Models for Recommendation Systems and Autonomous Systems

2.3. User Interaction Data and Evaluation Metrics for AI Models

2.4. AI Assistance with Deep Learning in Retail and E-Commerce

2.5. Future Directions, Challenges, and Ethical Considerations in AI

3.1. Data Collection on Shopping

• Amazon Customer Reviews
  Description: This dataset includes customer reviews for products on Amazon, capturing user opinions, ratings, and review text.
  Data Type: Textual data related to user reviews.
  Sample Data
  {
    “user_id”: “A3R5OBKS7OM2IR”,
    “review_id”: “R2G29SIF5K3M22”,
    “product_id”: “B00YD545CC”,
    “rating”: 5,
    “review”: “Excellent sound quality and very portable!”,
    “timestamp”: “2019-01-02”
  }

• Retail Dataset
  Description: IoT-based smart shopping cart data used for frequent itemset mining.
  Data Type: Transactional data capturing shopping behavior.
  Sample Data
  {
    “transactionID”: “T12345”,
    “items”: [“OxiClean Versatile Stain Remover”, “Tide Pods Laundry Detergent”],
    “timestamp”: “2022-01-15 08:45:00”
  }

• Shopping Intention at AI-Powered Retail Stores
  Description: Data on consumer intentions in AI-powered automated retail stores.
  Data Type: Survey data capturing shopping intentions.
  Sample Data
  {
    “respondentID”: “R56789”,
    “intention”: “Purchase”,
    “productCategory”: “Laundry”,
    “timestamp”: “2022-01-15 10:00:00”
  }

• Clickstream Data
  Description: Predicts online shopping behavior using clickstream data.
  Data Type: Clickstream data capturing user interactions on e-commerce websites.
  Sample Data
  {
    “sessionID”: “S12345”,
    “clicks”: [“home page”, “search: stain remover”, “click: OxiClean”],
    “timestamp”: “2022-01-15 09:30:00”
  }

• Shopping Queries Dataset
  Description: This dataset contains a large collection of shopping-related queries aimed at improving the efficiency and accuracy of product search algorithms. The dataset includes 130,000 queries, providing a rich resource for training and evaluating search models.
  Data Type: Textual data related to shopping queries.
  Sample Data
  {
    “example_id”: “12345”,
    “query”: “portable speaker with excellent sound quality”,
    “query_id”: “67890”,
    “product_id”: “B00YD545CC”,
    “product_title”: “High-Quality Portable Speaker”,
    “product_description”: “A high-quality, portable speaker with excellent sound quality.”,
    “product_bullet_point”: “Great sound, Long battery life, Compact design”,
    “product_brand”: “BrandName”,
    “product_color”: “Black”,
    “product_locale”: “US”,
    “esci_label”: “E” // E for Exact match, S for Substitute, C for Complement, I for Irrelevant
  }

3.2. Neutral Language Model

2.

BERT (Bidirectional Encoder Representations from Transformers)

BERT utilizes the Transformer architecture, comprising an encoder and a decoder. BERT exclusively employs the encoder component of the Transformer architecture. The architecture consists of numerous layers, with the largest version, BERT-Large, having up to 24 layers. Each layer is equipped with multiple attention heads, enabling the model to simultaneously focus on different segments of the text [48].

$$L={\sum }_{i=1}^{n}logP\left(x_i\right|x_1:\mathrm{i}-1,x_i+1:n)$$

The equation represents the loss function used by the BERT model. In this formula, $L$ denotes the overall loss, which is the sum of the logarithmic probabilities of each token $x_i$ given its surrounding context $x_1:i-1$ and $x_i+1:n$. This bidirectional approach allows BERT to understand the context of a word based on both its preceding and succeeding words, thereby enhancing the model’s ability to grasp nuanced meanings in the text.

Figure 3 depicts the sequential process that BERT employs to produce an output sequence. The procedure commences with the Input Sequence, in which unprocessed textual material is introduced into the model. The data are next fed into the Embedding Layer, where the input tokens are transformed into compact vectors that encode semantic information. The Positional Encoding layer incorporates positional information for each token in the sequence, enabling the model to take word order into account. The embeddings, which include positional encodings, are subsequently sent through Multiple Transformer Layers. These layers comprise multiple attention mechanisms and feed-forward networks. Ultimately, the analyzed data are converted into an Output Sequence, producing text that is both contextually dense and logically connected.

3.

T5 (Text-to-Text Transfer Transformer)

T5’s design is built upon the Transformer model, which comprises an encoder–decoder structure. The encoder analyzes the input text, while the decoder produces the output text. T5 can handle a range of NLP tasks, including translation, summarization, and question answering, by treating them as text-to-text problems [49].

$$y=f_{\theta }\left(x\right)$$

The equation represents the function used by the T5 model, where $y$ is the output sequence, $x$ is the input sequence, and $f_{\theta }$ denotes the model’s function parameterized by $\theta$. This encapsulates the T5 model’s approach to transforming an input sequence into an output sequence by treating various NLP tasks as text-to-text problems. The function $f_{\theta }$ processes the input text through its encoder and decoder structures to generate the desired output.

Figure 4 depicts the sequential process utilized by the T5 model to produce an output sequence. The procedure commences with the Input Sequence, which is introduced into the Encoder. The encoder analyzes the input text to generate an Embedded Sequence, which captures both the semantic and contextual information. The embedded sequence is thereafter transmitted to the Decoder, which employs both Cross-Attention and Masked Self-Attention methods to produce text that is aware of the context. Ultimately, the decoder generates the Output Sequence, which represents the model’s forecast derived from the input text.

4.

SAAS (Shopping Assistance Automatic Suggestion)

The SAAS model is a custom deep learning model designed specifically to provide shopping assistance through automated suggestions. The model combines features from GPT-3, BERT, and T5, and is fine-tuned on a comprehensive shopping dataset to deliver accurate, context-aware recommendations. The fine-tuning process involves several steps, including data preprocessing, model training, and evaluation [50].

$$y={\sum }_{i=1}^n{w}_i·f_{{\theta }_i}\left(x_i\right)+ϵ$$

The equation represents the prediction function used by the SAAS model. Here, $y$ denotes the output, $x_i$ represents the individual input features, $w_i$ are the corresponding weights, $f_{{\theta }_i}$ indicates the function applied to each input feature parameterized by ${\theta }_i$, and $\in$ is the error term. This equation encapsulates the model’s approach to combining multiple weighted features processed by different functions to generate the final output, accommodating the inherent variability and noise in the data [12].

• Data Preprocessing:
  • Normalization: The shopping datasets are normalized to ensure consistency. Fields such as User ID, Review, Product ID, Product Type, Action Type, and Rating are standardized.
  • Tokenization: Text data is tokenized using a tokenizer compatible with the underlying model architecture.
  • Padding and Truncation: Tokenized sequences are padded or truncated to a uniform length to ensure consistent input sizes for the model.
  • Data Splitting: The dataset is split into training, validation, and test sets to enable effective model training and evaluation.
• Model Training:
  • Transfer Learning: The SAAS model leverages pre-trained weights from GPT-3, BERT, and T5, utilizing their language understanding capabilities.
  • Fine-Tuning: The model is fine-tuned on the normalized shopping dataset. Fine-tuning involves adjusting the model’s weights to minimize a loss function (e.g., cross-entropy loss for classification tasks).
  • Hyperparameter Optimization: Parameters such as learning rate, batch size, and number of epochs are optimized to enhance model performance.
• Evaluation:
  • Performance Metrics: The model is evaluated using metrics such as accuracy, precision, sensitivity, specificity, F1 score, and runtime performance.
  • Validation: The performance on the validation set guides further tuning and adjustments.
  • Testing: The final model is tested on a separate test set to assess its generalization capabilities.
• Fine-Tuning Process:
  • Data Preprocessing: Normalize the dataset to ensure consistency in the input data.
  • Model Training: Use the preprocessed data to train the SAAS model, adjusting weights and biases to minimize the error function.
  • Evaluation: Test the model on a validation set and adjust parameters to improve performance.

3.3. Integration with the Shopping Dataset

• 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.
• 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.
• 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.
• 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.

• 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.

4.1. Model Performance Metrics

4.2. Suggestion System Performance

4.3. Analysis

5.1. Practical and Theoretical Implications

5.2. Summary of the Process:

5.3. Specifications

• Data Capture: The immediate collection of data from virtual reality activities, such as choosing items and doing experiments, is crucial for offering precise and prompt recommendations [53].
• The SAAS model performs data processing by utilizing its trained capabilities to analyze user preferences and produce appropriate recommendations.
• The recommendations produced are transformed into lifelike speech by sophisticated Text-to-Speech (TTS) technology, guaranteeing a seamless and coherent delivery by the non-player characters (NPCs).
• NPC Interaction: Non-player characters (NPCs) in the virtual reality (VR) environment are specifically programmed to engage with users in a manner that closely resembles normal human discourse. These NPCs offer suggestions and guidance in a way that imitates realistic human interaction [54,55].

5.4. Factors Must Be Considered during the Implementation Process

• Minimizing latency is essential for achieving a smooth user experience by ensuring efficient data processing and voice synthesis.
• Voice Quality: Clear and natural-sounding speech requires the use of high-quality TTS systems.
• NPC Behavior: NPCs should be programmed to display authentic behaviors and reactions, hence boosting the overall immersion of the VR environment.