# Comparative Analysis of SAAS Model and NPC Integration for Enhancing VR Shopping Experiences
## 3 Methodology
### Neutral Language Model  
This section describes the use of various deep learning models for natural language processing (NLP) in recommendation systems. We will leverage several existing models, such as GPT-3, BERT, and T5, and then create a custom model, Shopping Assistance Automatic Suggestion (SAAS), specifically tailored for shopping assistance. Each model will be trained and tested on our unified shopping dataset to compare their performance [46; 47]. Detailed descriptions, formulas, and flowcharts for each model are provided below. 1. GPT-3 (Generative Pre-trained Transformer 3) GPT-3, developed by OpenAI, is a highly sophisticated language model. It utilizes advanced deep learning methods to produce writing that closely resembles human language, using the information it is given as input. This model belongs to the Transformer lineage, which has brought about a paradigm shift in the domain of natural language processing (NLP) by efficiently addressing the challenge of managing long-distance relationships in textual data. The user's text consists of two references [1; 2]. \[\rho(x)=\Pi_{i=1}^{n}P(x_{i}|x_{1;i-1})\]  
The equation represents the sequence prediction process used by the GPT-3 model. In this formula, \(P(x)\) denotes the probability of the entire sequence \(x\), which is calculated as the product of the conditional probabilities of each token \(x_{i}\), given all preceding tokens \(x_{1}:i-1\). This approach allows the model to generate text that is coherent and contextually relevant by considering the preceding context for each token in the sequence. GPT-3 utilizes input data to produce an output sequence using the following method. The procedure commences with the Input Sequence, in which unprocessed textual data are received. The input is subjected to Tokenization, which involves dividing the text into separate tokens. Subsequently, these tokens are transmitted through the Embedding layer, where they undergo a transformation into compact vectors that encapsulate semantic information. The Transformer Layers utilize these embeddings to acquire contextual links between tokens. Ultimately, the processed data undergo a conversion that results in an Output Sequence, generating text that is both coherent and contextually suitable. The sequential advancement is depicted in Figure 2.
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, \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**User ID** & **Review** & **Product ID** & **Product Type** & **Action Type** & **Rating** \\ \hline A3R50 BKS7/OM2 IR & Excellent sound quality and & B00 YD545 CC & Electronics & Review & 5 \\  & very portable! & & & & \\ \hline B2Y12345678 & This product met all my expectations & A1B2C3D4E5f6 & Home Goods & Review & 4 \\ \hline C1D2E3F405 FH6 & Searching for new products is & & & & \\  & very intuitive & & & & \\ \hline D3E4F5G6H718 & Helpful suggestions & & & & \\  & and recommendations & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Sample User Interaction Data. Figure 2: The sequence prediction process used by the GPT-3 Model. 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]. \[L=\sum_{i=1}^{n}logP(x_{i}|x_{1}:\mathrm{i}-1,x_{i}+1:n)\]  
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.