## 5 Conclusions
### Theoretical Contributions and Practical Implications  
In this study, we have enhanced the recommendation performance by analyzing review helpfulness information through deep learning techniques and reflecting them in recommender systems. The theoretical implications of this study are as follows: First, the existing studies on personalized recommendation services used all reviews included in the item to extract the sentiment features and reflect them in the recommender system. However, user-written reviews include advertisements, falsehoods, and unknown content reviews [69]. In other words, if reviews are irrelevant to items and unhelpful to users, they can reduce recommendation performance. Therefore, we proposed a recommendation framework to classification review helpfulness information and reflect them in recommender systems. In this study, we have improved the recommender systems' performance by using the review helpfulness information. This result can contribute to the extended scope of the personalized recommendation service-related studies. Second, to evaluate the recommender system's performance considering the review helpfulness information, we compared the results considering the review helpfulness as well as not considering the review helpfulness. The experimental results showed that the recommendation performance was higher when considering review helpfulness information. Therefore, besides features, price, and users' sentiment, the review helpfulness information is essential in purchasing decision making. Furthermore, objective information such as the number of review helpfulness votes influences users' preference more than subjective user-written reviews.  
The practical implications of this study are as follows: First, we proposed a recommendation framework that classified the review helpfulness information and reflected them in the personalized recommendation services. We conducted several experiments and found that considering helpful reviews can enhance recommendation performance over traditional methods. Most e-commerce websites provide a module for writing reviews of items purchased by users. Nonetheless, few e-commerce websites have reflected the helpfulness information in reviews. Therefore, it needs to provide services that could evaluate the review helpfulness information. For example, if the review helpfulness information is evaluated with a high score, it can increase the review information value of the item by providing users with mileage or coupons. Second, most e-commerce websites have focused on item reviews and encouraged users to write reviews on items. We found that the quality of the review is more critical than the number of reviews when providing personalized recommendation services. Therefore, rather than increasing the number of reviews, it requires a strategy that encourages users to write high-quality reviews. Finally, the proposed recommendation framework in this study can apply to the various domains of e-commerce websites that provide review usefulness information. This enables the website to build more sophisticated recommendation services, providing decision support in many aspects, including marketing and user management. Therefore, e-commerce websites can increase the convenience and satisfaction of the users and expect sales growth.