A Research on TF-IDF-based Patent Recommendation Algorithm using Technology Transfer Data
(주)코리아스칼라
- 최초 등록일
- 2023.10.09
- 최종 저작일
- 2023.09
- 11페이지/ 어도비 PDF
- 가격 4,200원
* 본 문서는 배포용으로 복사 및 편집이 불가합니다.
서지정보
ㆍ발행기관 : 한국산업경영시스템학회
ㆍ수록지정보 : 산업경영시스템학회지 / 46권 / 3호
ㆍ저자명 : Junki Kim, Joonsoo Bae, Yeongheon Song, Byungho Jeong
목차
1. 서 론
1.1 연구 배경
1.2 연구개발 필요성
2. 이론적 배경
2.1 추천 모델링
2.2 아이템 기반 협업 필터링
2.3 TF-IDF 모델
2.4 콘텐츠 기반 추천시스템
2.5 하이브리드 추천시스템
3. 실험과정
3.1 추천 모델링 구축을 위한 자료수집 및 전처리
3.2 아이템 기반 협업 필터링 모델 구축
3.3 콘텐츠 기반 TF-IDF 분석 모델 구축
3.4 기술이전 데이터 활용 기술 추천 알고리즘 구축
4. 실험 결과 도출
5. 결 론
5.1 연구 수행 결과
5.2 시사점 및 향후 연구방향
Acknowledgement
영어 초록
The increasing number of technology transfers from public research institutes in Korea has led to a growing demand for patent recommendation platforms for SMEs. This is because selecting the right technology for commercialization is a critical factor in business success. This study developed a patent recommendation system that uses technology transfer data from the past 10 years to recommend patents that are suitable for SMEs. The system was developed in three stages. First, an item-based collaborative filtering system was developed to recommend patents based on the similarities between the patents that SMEs have previously transferred. Next, a content-based recommendation system based on TF-IDF was developed to analyze patent names and recommend patents with high similarity. Finally, a hybrid system was developed that combines the strengths of both recommendation systems. The experimental results showed that the hybrid system was able to recommend patents that were both similar and relevant to the SMEs' interests. This suggests that the system can be a valuable tool for SMEs that are looking to acquire new technologies.
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