###### Abstract  
As the e-commerce market grows worldwide, personalized recommendation services have become essential to users' personalized items or services. They can decrease the cost of user information exploration and have a positive impact on corporate sales growth. Recently, many studies have been actively conducted using reviews written by users to address traditional recommender system research problems. However, reviews can include content that is not conducive to purchasing decisions, such as advertising, false reviews, or fake reviews. Using such reviews to provide recommendation services can lower the recommendation performance as well as a trust in the company. This study proposes a novel review of the helpfulness-based recommendation methodology (RHRM) framework to support users' purchasing decisions in personalized recommendation services. The core of our framework is a review semantics extractor and a user/item recommendation generator. The review semantics extractor learns reviews representations in a convolutional neural network and bidirectional long short-term memory hybrid neural network for review helpfulness classification. The user/item recommendation generator models the user's preference on items based on their past interactions. Here, past interactions indicate only records in which the user-written reviews of items are helpful. 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. The helpfulness classification model is trained about limited reviews utilizing helpfulness scores. Several experiments with the Amazon dataset show that if review helpfulness information is used in the recommender system, performance such as the accuracy of personalized recommendation service can be further improved, thereby enhancing user satisfaction and further increasing trust in the company. collaborative filtering; convolutional neural networks; review helpfulness; personalized recommendation services +
Footnote †: journal: MedicalCollaborative Filtering (CF) is the state-of-the-art recommendation model, which identifies users' and items' interactions and provides personalized recommendation services from quantitative information such as clicking, rating, and viewing [7; 8; 9; 10; 11]. However, such a methodology only models the action pattern without capturing qualitative preferences such as a motivation and a purchase reason for the item [12; 13]. Therefore, such methodologies can raise the issue where recommendation performance decreases [1; 14]. Recently, many studies have been conducted using various additional information to address the limitation of existing studies. Most e-commerce websites provide review modules where the users write reviews of their purchased items. According to [PERSON] [15], 88% of users make purchasing decisions by referring to reviews when purchasing products. A review text can be helpful because it includes specific and reliable information such as the reason for purchasing and evaluating the item [14]. However, the existing studies of personalized recommendation services using reviews mainly focused on extracting sentiment features or exploring several attributes and utilized them by combining with the CF approach [16]. However, reviews include unhelpful content for inconducive purchasing decisions such as advertising, unmeaningful content, or fake reviews [17]. It is therefore indisputable that providing recommendation services without any considerations of the quality of the review may decrease recommendation performance [18]. In order to address the limitation of the existing study problems, this study aims to review helpfulness information in the personalized recommendation service that can affect users' purchase decisions. Recently, the number of reviews of items has been increasing as more users purchase items on e-commerce websites.