## 2 Related Work
### Cbr  
CBR was proposed by [PERSON] et al., and it led to significant advances in the field of artificial intelligence [13]. CBR imitates an individual's approach to solving new problems based on previously experienced problems. Each experience is represented as a unit called a \"case\" and its solution is stored and used to solve current problems [14]. Thus, CBR recognizes knowledge as experience, and that knowledge consists of encapsulated case libraries [15].  
The CBR cycle consists of four stages: case retrieval, case reuse, case revision, and case retention (Figure 1). In the case retrieval stage, the most similar previous cases are retrieved and compared with the current problem. To find similar cases, we must extract the attributes or features that can be used to measure similarity and have a good understanding of the domain of the problem. Therefore, appropriate similarity measurement and matching algorithms are used for the features of each domain and the attributes of the cases. For example, in an ontology, matching algorithms can be applied to the features of instances in the domains to measure the similarities between the instances. In addition, similarity can be calculated using various measurements such as rule-based or concept similarity measurements [16; 17]. To solve the current problem, CBR enters the case reuse stage, which reuses the solutions of retrieved cases. If necessary, the solutions are revised by modifying the retrieved cases or their solutions to develop a more optimized solution. After the case revision stage, the validated solution is stored as a case that can be used for future problems.  
The CBR method is relatively simple and can be applied in fields in which the problem area is not standardized. One such field is recommendation systems, in which CBR is used because human interests and tastes are diverse and cannot be expressed by rules. [PERSON] et al. proposed an application that recommends financial products using CBR methods. A method for measuring similarity according to each element was proposed by establishing the necessary assets and information according to the target area of finance [19]. Matching algorithms and heuristic experiments were used to set thresholds and measure similarities according to the data characteristics. Furthermore, the K-nearest neighbor method was used to reuse the solutions of the retrieved cases and solution revisions were based on the user's subjective judgment. [PERSON] introduced a recommendation system using CBR for sustainable smart city development [20]. They selected features to use in the search for specified development plan cases, adopted comparative and Levenstein distance algorithms for keyword search [21], and proposed a method to train case bases using the measured satisfaction regarding the proposed solution. [PERSON] et al. proposed a recommendation system that uses an ontology and CBR [15]. Services were represented using an ontology, and CBR was applied to recommend services, because symptoms, diseases, related departments, and physician-related data types are intertwined in complex ways because of the characteristics of healthcare systems and data.  
Research using CBR requires different components to build a system depending on the domain, and an appropriate similarity metric is required depending on the characteristics of the elements. Alternatively, various artificial intelligence techniques can be combined. Furthermore, if knowledge or data are sufficiently described, solutions can be derived without special reasoning if a given problem is similar to or equal to the previous case. This also ensures the problem is solved quickly. Revision methods for optimizing reused solutions are among the main challenges. In addition, it is necessary to propose a case-retention method that enables efficient maintenance and management.  
Figure 1: Cycle of CBR (adapted from [18]).