## 2 Related Work  
### Collaborative Filtering  
A personalized recommendation service uses ratings, purchase history, and browsing history to provide products or services to users [5]. Furthermore, such a personalized recommendation service provides convenience for users who have difficulty making purchasing decisions on several types of items and services. Global companies such as Netflix, Amazon, and Google generate revenues by introducing personalized recommendation services in e-commerce to support users' decision making [6; 21]. Therefore, personalized recommendation services are used in various industries, and related studies are conducted continuously [1; 22]. Currently, CF algorithms are widely used in academia and industry with excellent recommendation performance [10; 23].  
CF is a recommended approach based on the similarity between users or items, assuming that users with preferences for the same item have similar preferences for other items [24; 25]. CF algorithms are divided into memory-based CF and model-based CF. Memory-based CF is divided into two categories: UBCF and Item-Based CF (IBCF) [26]. UBCF is a method of recommendation items purchased by users with similar preferences to the recommended users. The recommendations are provided through three stages: First, measure the similarity between users to select neighbor users similar to the recommended users. Next, calculate the item preference prediction rating for the recommended user. Finally, the product with the highest preference prediction value is recommended to the user [27]. The IBCF recommendation method is that users prefer items similar to historical purchases items. In other words, the target user recommends the most similar items based on the historical purchase's items. Model-based CF uses the previous datasets to train a model with machine learning or data-mining techniques to improve the performance of the CF method [28]. These techniques can quickly recommend a series of items for the fact that they use a precomputed model, and they have proved to produce recommendation results that are similar to neighborhood-based recommender techniques [29]. In addition, the techniques need to be used in the categorization model if the user preference is categorical data. Suppose user preference is continuous data, techniques such as SVD, NCF, and Regression, should be used [30]. Despite the success of the CF-based recommender system, some problems have been revealed, such as the following: This method essentially recommends items based on users' past purchasing history and preferences. However, recommender systems experience a cold-start issue in new users, as there is insufficient data available to measure similarity; therefore, user preferences cannot be predicted [25]. Furthermore, a first-start issue exists in which users' preferred items are not recommended because they have not yet been purchased [25].  
The existing studies on the CF have predicted users' preferences, which used quantitative data such as clicking, rating, and viewing. However, such a traditional approach without understanding behavior motivation can reduce the recommendation performance. To address the limitations of CF approach, most studies use additional information. Typically, review text is among them. In this study, we propose a framework considering the review text, which represents the unstructured data to improve the limitations of existing CF approaches. We hope to address the limitations of the CF approach, which only considers quantitative information, to provide excellent recommendation performance.