###### Abstract
In Table 1, users can identify the product's characteristics from reviews and utilize much of the information in the purchase decision-making process. However, users cannot refer to all reviews in the purchase decision-making process. Therefore, users have difficulties in exploring helpful reviews in the product purchase process. To address this issue, Amazon provides a review helpfulness voting module to confirm whether reviews are helpful in the purchase decisions process since 2007 [19]. The ranking of reviews is sorted through the number of review helpfulness votes, and the most voted reviews are marked at the top of the list. Because the review helpfulness information has an significant role in the user's purchase decision-making process, and it plays an essential role in providing personalized recommendation services [20]. This study proposes a novel reviews helpfulness-based recommendation methodology (RHRM) framework that can support users' purchasing decisions in personalized recommendation services. Our framework consists of three phases: a review semantics extractor, a user profile producer, and a user/item recommendation generator. First, in the review semantics extractor phase, we generate review representations hierarchically for review helpfulness classification. We first extract the review's semantic representation using Convolutional Neural Network (CNN), then obtain two-way representations using the Bi-directional Long Short-Term Memory (BiLSTM) attention network and combine such representations to generate a final semantic representation. Since many reviews do not have helpfulness scores, we first propose a helpfulness classification model to reflect the review helpfulness that significantly impacts users' purchasing decisions in personalized recommendation services. This CNN-BiLSTM hybrid model utilizes generated semantic representation to classify the helpfulness of the reviews. After review helpfulness \begin{table}
\begin{tabular}{c c c} \hline \hline
**Item Category** & **Item Name** & **Number of Reviews** \\ \hline Automotive & THISWORK Car Vacuum Cleaner & 170,864 \\ Sports and Outdoors & Iron Flask Sports Water Bottle & 68,956 \\ Pet Supplies & Amazon Basics Dog and Puppy Pads & 125,848 \\ Electronics & Echo Dot (3 rd Gen) & 895,176 \\ Home and Kitchen & Mellanni Queen Sheet Set & 241,804 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Number of reviews received by Amazon Best Sellers items. information is classified, we send it to the user profile producer phase. Second, the user profile producer phase also utilizes helpfulness information classification results to update user profiles based on helpful reviews that the user has written about the item. Here, the updated user profile contains only user/item interactions that correspond to written helpful reviews by the user. Finally, the user/item recommendation generator utilized the most popular CF techniques to model users' preferences on items based on their interactions profile produced in phase 2. We applied User-Based CF (UBCF), Singular Value Decomposition (SVD), and Neural Collaborative Filtering (NCF), the most popular models in CF techniques. We have conducted extensive experiments with the Amazon dataset. The results demonstrate that our framework can effectively improve the performance recommendations when reflecting the review helpfulness information. The contributions that this paper have made are summarized as follows:  
* This study first proposes the RHRM framework that has filtered the review helpfulness and reflected upon personalized recommendation services. It can enhance the recommendation performance because it reflects the purchasing behavior of the users who consider reviews when purchasing items. * This study has built a review helpfulness classification model using the combined CNN and BiLSTM that demonstrates excellent performance in the Natural Language Processing (NLP) study. We confirm the advantages of the combined CNN-BiLSTM hybrid model in semantic representation extraction through various experiments.