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비선호 패턴 분석 기법을 활용한 R&D정보 추천 모델 개선
조우승(Wooseung Jo),이종원(Jongwon Lee),김태현(Tae-Hyun Kim),신동구(Donggu Shin),정회경(Hoekyung Jung) 한국정보통신학회 2024 한국정보통신학회논문지 Vol.28 No.2
National R&D information includes various contents generated by national R&D projects and is serviced by the National Science and Technology Knowledge Information Service (NTIS). NTIS provides a search engine-based search method. This method provides search results by sorting them based on similarity ranking, and there is a structural limitation in that users have to search the initial search results several times or filter them to find the information they want. To address these limitations, this paper describes the core model of the R&D recommendation information service that recommends content by analyzing user usage patterns and characteristics of content. This model is a hybrid model that combines collaborative filtering, content-based filtering, and deep learning models, which are widely used in the field of recommending information, and is intended to further utilize preferred/non-preferred pattern analysis techniques derived by analyzing the usage rate of recommended information for each user. Through this, users are expected to receive improved recommendation results.
김태현(Tae-Hyun Kim),조우승(Wooseung Jo),신동구(Dong-Gu Shin),이종원(Jongwon Lee) 한국정보통신학회 2023 한국정보통신학회 종합학술대회 논문집 Vol.27 No.1
22년도 기준, 국가연구개발(R&D) 사업은 사업예산 29.7조, 수행과제수 7.6만건 규모로 10년전 대비 각 86%, 52% 증가하였고, 이에 따라 국가R&D에 참여하는 연구자 및 연구기관도 증가하는 추세이다. 연구자 정보의 경우 국가 차원의 국가연구자번호 발급과 관리를 통해 국가R&D 참여정보를 체계적으로 관리하고 있으나, 연구개발기관 정보의 경우 기관명과 사업자등록번호로 식별하는 수준으로 관리되어 연구기관 정보를 중심으로 한 정보의 등록·관리 체계가 부재하고, 따라서 기관 관점의 정보활용 수요에 대응하기에 부족한 실정이었다. 이러한 문제점을 해결하기 위해 본 논문에서는 국가R&D 정보로부터 기관정보를 추출·가공하고 외부 정보를 활용해 검증·보강함으로써 국가R&D 참여연구자가 신뢰할만한 연구개발기관 정보를 구축하였다. 향후 이를 기반으로 국가연구개발기관에 대한 다양한 분석정보를 제공할 수 있는 기반을 마련하였다. As of 2022, the budget allocated to national research and development (R&D) projects was 29.7 trillion won, and the number of projects performed was 76,000, an increase of 86% and 52%, respectively, compared to 10 years ago. Accordingly, the number of researchers and research institutions participating in national R&D is also increasing. In the case of researcher information, national R&D participation information is systematically managed through issuing and managing national researcher numbers at the national level, but research and development institution information is managed at a level that identifies by institution name and business registration number. Therefore, there was a lack of a system for registering and managing information centered on research institution information. And it was insufficient to respond to the demand for information utilization from the perspective of the institution. To solve these problems, this paper constructed a reliable research and development institution information for national R&D participants by extracting and processing institution information from national R&D information and verifying and supplementing it using external information. Based on this, we have prepared a foundation that can provide various analysis information on national research and development institutions in the future.