## 2 Related Work
### Review-Based Recommender System  
Reviews are qualitative data as they refer to users' written review about the item information or experience. Such reviews are an important feature in which users can represent detailed expressing opinions about the items [31]. Therefore, most studies develop various recommender systems using reviews to overcome existing recommender systems' limitations that only use quantitative data. [PERSON] et al. [32] applied sentiment analysis to movie reviews and developed a model to estimate the review's sentiment. Then, the calculated sentiment index from models and is reflected in the CF. It is the first study that applies user reviews to recommender systems. However, it only considered qualitative information and the review's sentiment. Therefore, it provides for higher recommendation performance when considering both qualitative and quantitative data simultaneously. [PERSON] et al. [33] performed sentiment analysis to user-written reviews and classified users as intuitionists and pessimists. The performance of CF was higher when users are classified as intutitials and pessimists than traditional CF. It is significant in that the user-written reviews were classified. However, review contents were not reflected in recommender systems, and there is a chance to reduce the loss of information. [PERSON] et al. [34] proposed an urCF (User Review enhanced Collaborative Filtering) recommendation methodology that reflected the review. It used the reviews about 32 movies in the movie review ontology of [PERSON] and [PERSON] [35]. The reviews' features were derived using FF-IRF (Feature Frequency-Inverse Review Frequency), similar to TF-IDF (Term Frequency-Inverse Document Frequency). The user's sentiment polarity is reflected in each review's features, then the similarity between users is calculated, and CF algorithms are proposed based on them. The results showed that the proposed methodology applied to Yahoo Movies data improved the prediction accuracy from 6.18% to 8.24% over traditional CF methods. The prediction performance results were excellent, but it disregarded the content of reviews. [PERSON] and [PERSON] [36] considered user-written reviews to improve the performance of CF. They verified the effectiveness of the proposed methodology applied to smartphone app review data and quantified reviews through text mining. Their results show that reflecting review between users' similarity in CF was better than traditional CF on performance. [PERSON] et al. [37] proposed a recommendation algorithm that combined user-written reviews and ratings to reflect on the CF. They established a sentiment dictionary using movie review data. The sentiment index of reviews was derived from the sentiment dictionary, and new ratings generated by combining sentiment index with ratings are reflected in the CF. They proposed a new methodology that combines reviews and ratings. However, they only reflected the positive and negative sentiment of the reviews and did not consider the content or helpfulness of the review.  
The existing recommender system studies using review data follow the same paradigm, in which the historical reviews are aggregated into a long document. Then, they focused on extracting the sentiment features or performed topic modeling analysis on the reviews text. However, the review text may include unhelpful content for users to make decisions, such as advertisements and fake reviews. Thus, having disregarded the content or helpfulness of the review, the recommendation performance decreases [38]. This study proposes an RHRM framework that provides personalized recommendation services by producing user profiles through filtering helpful information reviews. We try to filter high-quality reviews that help users make their decisions.