# Comparative Analysis of SAAS Model and NPC Integration for Enhancing VR Shopping Experiences
## 3 Methodology
### Neutral Language Model
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]. \[y=f_{\theta}(x)\]  
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. Figure 3: The sequence prediction process used by the BERT Model. 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]. \[y=\sum_{i=1}^{n}w_{i}\cdot f_{\theta_{i}}(x_{i})+\epsilon\] 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]. 1. 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. 2.