기술이전 데이터를 활용한 TF-IDF기반 특허추천 알고리즘 연구
A Research on TF-IDF-based Patent Recommendation Algorithm using Technology Transfer Data
0
조회
0
다운로드
1
인용
0
참고문헌
키워드
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.
Abstract
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.
참고문헌 (17)
- How will the govern-ment improve the technology commercialization of SMEs?, "Ahn, S. K", KISTEP, 1~42, pp.2020-07 (2020)
- Baazeem, I., "Analysing the Effects of Latent Semantic Analysis Parameters on Plain Language Visualisation", Master’s, pp.2015
- Burke, R., "Hybrid recommender systems : Survey and experiments", User modeling and user-adapted inter-action, 12(4), pp.331~370 (2002)
- Chen, Y. C., "A collaborative filtering recommendation system with dynamic time de-cay", The Journal of Supercomputing, 77, pp.10~ (2021)[DOI]
- Cory, L., "Advanced Machine Learning with R: Tackle data analytics and machine learning challenges and build complex applications with R 3.5" (2019), Packt Publishing
- Imran, A., "40 Algorithms Every Programmer Should Know: Hone your problem-solving skills by learning different algorithms and their implementation in Python", pp.1~382 (2020), Packt Publishing
- "Kakao AI Recommendation: Content-based filtering in Kakao (Content-based Filtering in Kakao)" (2021)[URL]
- "Smart-tech bridge" (2022)[URL]
- "Korea intellectual property rights information serv-ice" (2022)[URL]
- "Network for Agri-forest-food-Tech Information" (2022)[URL]
- Public Research Institutions (Universities and Research Centers) Technology Transfer and Commercialization Survey Report, "Ministry of Trade, Industry and Energy", ~ (2021)
- Salton, G., "Term-weighting approaches in automatic text retrieval", Information Processing &Management, 24(5), pp.513~523 (1988)
- 손지은, "추천 시스템 기법 연구동향 분석", 대한산업공학회지, 41(2), pp.185~208 (2015), 대한산업공학회[DOI]
- Song, Y. H, "Exploring the Factors Affecting Commercialization in National R&D Technology Transfer: Focusing on Non-Food Agriculture Sector", Fall Conference of the Korea Society of Innovation, pp.109~126 (2022)
- "StackOverflow, Choice between an adjusted cosine sim-ilarity vs regular cosine similarity" (2022)[URL]
- Vijay K. Jolly, "Commercializing New Technologies:Getting From Mind To Market", pp.1~410 (1997), Harvard Business Press
- Yu, M., "A model-based collaborate filtering algorithm based on stacked AutoEncoder", Neural Computing and Applications, 34, pp.10~ (2022)[DOI]