## 4 Proposed RSII Model
### The Teacher Module  
In this section, we will discuss the working process and significance of the teacher module in detail. As is well known, reviews are real evaluations written by users themselves, expressing their true thoughts at a certain moment. Additionally, reviews have the following characteristics: (1) Subjectivity: reviews are the personal opinions and evaluations of users on a certain thing or event; thus, they are subjective. (2) Evaluative: reviews \begin{table}
\begin{tabular}{c c} \hline \hline
**Notations** & **Definitions** \\ \hline \(d\) & The embedding size \\ \(c_{ij}^{1}\) & The quantized value of \(c_{ij}\) \\ \(c_{ij}^{2}\) & The semantics of \(c_{ij}^{1}\) \\ \(c_{ij}^{3}\) & The reconstruction vector of \(c_{ij}^{1}\) \\ \(C_{i}^{j}\) & All reviews of the \(i\)-th user excluding \(c_{ij}\) \\ \(C_{I}^{j}\) & All reviews on the \(j\)-th item excluding \(c_{ij}\) \\ \(c_{ij}\) & The generated semantic features of \(c_{i,j}\) reviews \\ \(O_{ij}\) & The ID features of the \(i\)-th user and \(j\)-th item \\ \(a_{ij}\) & The relative coefficient between \(O_{ij}\) and \(\hat{c}_{ij}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._  
Figure 3: The framework of the RSII model. usually contain evaluations on a certain thing or event, including its quality, advantages, disadvantages, etc. Therefore, it can be seen that target reviews are important for predicting the ratings between the target users and the target items. However, in practice, it is difficult to obtain the target reviews promptly. Therefore, it is necessary to generate target reviews indirectly for use in the recommendation process. The teacher module designed in this paper, combined with data division, could solve this problem. In this paper, based on the data processing process of the TransNets model, the data was divided as follows: when the model predicted the rating of the \(i\)-th user on the \(j\)-th item, \(c_{i,j}\) was a target review, \(C_{i}^{J}\) was all reviews of the \(i\)-th user excluding \(c_{i,j}\), and \(C_{i}^{J}\) was all reviews on the \(j\)-th item excluding \(c_{i,j}\). Although the target at test time, \(c_{i,j}\) was available during training. As such, the teacher module was only used in model training stage. \(c_{i,j}\) first obtained quantized value \(c_{i,j}^{1}\) after word embedding. \(c_{i,j}^{1}\) was then fed into the encoder to get \(c_{i,j}^{2}\). \(c_{i,j}^{2}\) represents the semantics of \(c_{i,j}^{1}\). \[c_{i,j}^{1}=G(c_{i,j}) \tag{1}\]  
\[c_{i,j}^{2}=CNN(c_{i,j}^{1}) \tag{2}\]  
where \(G\) denotes word embedding and \(CNN\) denotes CNN text processing. Currently, there are widely popular methods in mining semantics, such as TextCNN [28], TextRNN, paragraph vector, and BERT [29]. With the current research on review-based recommendation algorithms, this paper took the mainstream TextCNN processing approach. This article took a vector \(P\) of review length \(Q\) as an example to elaborate the \(CNN\) used in this article. \(CNN\) is composed of convolutional, pooling, and fully connected layers. The convolutional layer consists of \(K\) neurons that generate new features by applying convolutional operators to \(P\). The \(j\)-th convolution kernel \(K_{j}\in\mathbb{R}^{d\times q}\) in the convolutional layer performs convolutional operation on a window of size \(q\), as shown in the following formula. \[p_{j}=actf\big{(}P*K_{j}+b_{j}\big{)} \tag{3}\]  
where, \(*\) represents the convolution operation, \(b_{j}\) is the bias term, and \(actf\) is the activation function, which uses Rectified Linear Units. The formula for Rectified Linear Units as the activation function is as follows:  
\[f(x)=max\{0,x\} \tag{4}\]  
Then, the max-pooling operation was performed on the feature map, and this article used the maximum value as the corresponding feature \(h_{j}\) of the convolution kernel. The formula is as follows:  
\[h_{j}=max\{p_{1},p_{2},\cdots,p_{Q-q+1}\} \tag{5}\]  
Equations (3)-(5) describe the operation of a single convolution kernel.