## 3 Designing a Big Data Evaluator Recommendation System  
Although evaluator recommendation systems designed to select evaluators for filed projects have been created and operated continuously, the previous systems have focused only on showing a list of evaluator candidates. Thus, these systems play a larger role in presenting researchers in the specific areas of study and scanning their achievements as an auxiliary arm for evaluator recommendations than in finding appropriate evaluators for the projects.  
The previous method that identifies researchers suitable for project proposals consists of matching researchers to the correct areas by comparing the categories of research the submitter selects in the application and the code of the area of evaluation that the candidate chooses. This method, which matches a project with an evaluator in a simple manner without causing contention (because it is the area chosen by the applicant), has limitations due to the categorization of areas.  
First, because dynamic areas of research comprise real-time changes, data cannot be comprehended only through regular updates of the data. Moreover, currently, many studies take the form of convergence research and its variations cannot be contained in these classifications. Second, an applicant (or an evaluator candidate) may not select the appropriate research areas in the application. As there are diverse areas, including the area that a project proposal applies for, classification is sometimes difficult. The wide-ranging variations may not be captured in the categorization of the areas or the researcher may select an area that is different from the one indicated on the application (because, e.g., the area is more likely to be supported). Thus, after selecting a research area in high demand, a project evaluator, who is unrelated to the person's actual research topic may be selected. Third, the selection of evaluators based on the categorization of areas may tap researchers who flaunt their achievements and recognition in the areas instead of those who are most suitable for the submitted projects. Researchers who flaunt their achievements and recognition in specific areas may not be those who can best evaluate specific projects. Finally, a recommender's subjective preferences (such as a preference for specific journals or preferential treatment of patents) may be reflected in the selection of evaluator candidates.  
Evaluator recommendations in the big data recommendation system, as suggested in this study, break from the previous selection method through the categorization of areas that identify pairsof the most similar project applications and evaluator candidates. The system compares keywords extracted from the applications through the big data system and the major keywords extracted from the data in the full text of the diverse achievements fo the evaluator candidates. Identifying evaluator candidates who register high levels of similarity with project applications and selecting evaluators by determining priorities based on their similarities not only enables the selection of evaluators who are the most suitable for the submitted projects, but also improves conventional practices, such as three-fold recommendations, organization of groups, and determination of priorities.