## 3 RHRM Framework
### Phase 1: Review Semantics Extractor
\[c_{j}=\phi(E*K_{j}+b_{j}), \tag{2}\]  
where \(*\) indicates convolution operator, \(K_{j}\in R^{k\times m}\) indicates the parameter of the filter kernel, and \(k\times m\) denotes kernel size. \(b_{j}\) is represented bias, and \(\theta\) is the activation function \(ReLU\), which is defined as Equation (3). \[relu(x)=max(0,x) \tag{3}\]  
We add the max-pooling layer to the output of the convolution operation to retain the main semantics and suppress noise. The max-pooling operation is defined as Equation (4). \[O_{j}=\max([c_{1,}c_{2},\cdots,c_{(l-t+1)}]) \tag{4}\]  
This study applied multiple filters of different sizes to extract the various semantic feature included in the review. Finally, the output of the convolutional layer is as Equation (5). \[O=[o_{1},\ o_{2},\ \cdots,\ o_{n}] \tag{5}\]  
The third layer is an attention network. Each vector in the convolution layer output denotes the time step of the BiLSTM model. BiLSTM consists of two components: forward LSTM and backward LSTM. The forward LSTM captures the review semantic in the path from left to right, and the backward LSTM captures the sequence feature from right to left. This study defines the outputs of the forward and backward LSTMs as \(\overset{\rightarrow}{S}_{t}\) and \(\overset{\leftarrow}{S}_{t}\), respectively. We applied Bi-LSTM for processing all terms in the path sequence to obtain two separate hidden state sequences. Let the defined input sequence \(\{o_{1},\ o_{2},\ \cdots,o_{n}\}\),the forward LSTM generate hidden states \(\left\{\overset{\rightarrow}{S}_{1},\overset{\rightarrow}{S}_{2},\ \cdots, \overset{\rightarrow}{S}_{t}\right\}\), and the backward LSTM generate hidden states \(\left\{\overset{\leftarrow}{S}_{1},\overset{\leftarrow}{S}_{2},\ \cdots, \overset{\leftarrow}{S}_{t}\right\}\). \[ \begin{array}{c}\overset{\rightarrow}{S}_{t}=\text{LSTM}(\overset{ \rightarrow}{S}_{t-1},O_{t})\\ \overset{\leftarrow}{S}_{t}=\text{LSTM}(\overset{\rightarrow}{S}_{t-1},O_{t}) \\ m=[\overset{\rightarrow}{S}_{i};\overset{\leftarrow}{S}_{1}]\end{array}  \tag{6}\]  
The BiLSTM connects the last hidden state of the forward LSTM with the first hidden state of the backward LSTM to generate the final representation. The embedding vector \(m\) consists of both forward and backward information of the path to efficiently capture the orderings. Finally, to highlight the importance of different words to the classification of review helpfulness, we added the attention mechanism layer in the CNN-BiLSTM hybrid model to further extract review features and highlight the review-helpfulness-related information. This study belongs to the feed-forward attention mechanism, defined as Equation (7). \[ \begin{array}{c}h_{t}=\sigma(m_{i})\\ a_{t}=\frac{\exp(h_{t})}{\sum\limits_{i=1}^{n}\exp(h_{t})}\\ Q=\sum\limits_{i=1}^{m}a_{t}\cdot m_{i}\end{array}  \tag{7}\]  
where \(m_{t}\) indicates the eigenvector output of the BiLSTM layer and \(\sigma\) is the attention learning activation function _tanh_. \(h_{t}\) is the weight of the calculated generated attention. \(a_{t}\) is the matching score indicating how well the model participates in the path when responding to a query relation. The weighted sum operation uses the _SoftMax_ function for normalization to generate an attention probability. \(Q\) indicates a fusion feature of the representation multiplied by the probability of attention and the hidden state semantics encoding \(m_{t}\). Then, assign attention weight using the sum of weights. The objective of this model is to compute the probability of the helpfulness score based on the semantic feature extracted from the review and classify the results, which can be defined as Equation (8). \[Y=\theta(W_{s}\cdot Q+b_{s}), \tag{8}\]  
where \(\theta\) indicates the _Sigmoid_ activation function, \(W_{s}\) indicates the weight matrix, and \(b_{s}\) indicates the bias. Finally, the smectic input feature of review is classified as 0 or 1 and returned as output. A value of 0 output indicates that the review is unhelpful, and a value of 1 indicates a helpful review.