## 3 RHRM Framework
### Phase 1: Review Semantics Extractor  
The first phase constructs a CNN-BiLSTM hybrid model to classify review helpfulness information. The architecture overview of the CNN-BiLSTM hybrid model is shown in Figure 2. This study builds a CNN-BiLSTM hybrid model with excellent classification performance in NLP studies to classify review helpfulness [52; 53]. CNN can reduce the input features for prediction, and the correlation between each word and a final classification is not the same for all input words [54; 55]. The BiLSTM is utilized for encoding long-distance word dependencies effectively [52; 53]. Due to each of these advantages, various types of hybrid CNN-BiLSTM models have been proposed [40; 50; 52; 53; 56]. The CNN-BiLSTM hybrid model applied in this study was motivated by [PERSON] et al. [51] and [PERSON] et al. [50]. Existing models mainly used a combination of a single CNN network and a single BiLSTM network. The model was either used as a regression model to predict numeric values or applied to multiple classification problems. Following the common single model combination strategy, we applied multiple filter kernels and added a new attention mechanism layer to extract the review text's semantic representation elaborately [47; 57]. After generating a review-level semantic representation, the model classifies the helpfulness information for each review. In this study, we gave \(\mathcal{R}=\{r_{1},\,r_{2},\,\cdots,r_{n}\}\) as a dataset for constructing a CNN-BiLSTM hybrid model with the attention mechanism. Each review contains five attributions [P, U, C, M, H], where P indicates the item features, U indicates reviewer features, C indicates textual features, and M indicates metadata features (e.g., ratings and timestamp). H indicates the helpfulness score that is measured as the ratio of helpful votes to the total votes, where \(H\ \in\ [0,1]\). Let F as a \(n\times m\) review feature matrix, where n is the number of reviews in the dataset and m  
Figure 2: The architecture of CNN–BiLSTM hybrid model with the attention mechanism. features. \(Z\) is an embedding vector of the predicted value for all reviews, where \(Z_{i}\) represents whether a review is helpful or not. Finally, we define a helpfulness threshold value \(\Theta_{1}\) and \(\Theta_{2}\). Therefore, \(Z_{i}\) is calculated as follows:  
\[Z_{i}= \begin{cases}1,&\text{if }H_{i}>\Theta_{1}\\ 0,&\text{if }H_{i}<\Theta_{2}\end{cases}  \tag{1}\]  
This study constructs a CNN-BiLSTM hybrid model that minimizes the prediction error of \(Z\) given \(F\). The trained model is utilized to predict the helpfulness score of new review with unknown or unidentified helpful scores. The CNN-BiLSTM hybrid model consists of three layers. The first layer is word embedding. Let \(R_{u,i}=\{w_{1},w_{2},\cdots,w_{n}\}\) be a review text, which indicates that the user u has written the review to item i, where n is the length in the review. Many existing text-mining models were mainly applying the one-hot encoding method to convert each word into a vector. However, such a method has a data sparsity issue where the matrix dimensions are too large, and most of the vector values are filled with zero. In this study, each word included in the review was converted into a vector type through the word embedding layer [57]. Thus, this study has applied word embedding \(f\) : \(w_{n}\to R^{D}\) for each word in the review, and then each word is represented as a dense vector. Then, the review text is represented by a matrix \(E\in R^{n\times d}\), where d is the dimension of the word embedding vector. The second layer is a multichannel convolutional layer. It extracts the word-level semantic representation from the review text through different sizes filters. Then, it adopted a filter \(K_{j}\) with a sliding window to performing a convolution operation. The convolution operation process can be defined as shown in Equation (2).