Recommendation system for technology convergence opportunities based on self-supervised representation learning
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DOI: 10.1007/s11192-020-03731-y
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Cited by:
- Park, Mingyu & Geum, Youngjung, 2022. "Two-stage technology opportunity discovery for firm-level decision making: GCN-based link-prediction approach," Technological Forecasting and Social Change, Elsevier, vol. 183(C).
- Haochuan Cui & Tiewei Li & Cheng-Jun Wang, 2023. "Climbing up the ladder of abstraction: how to span the boundaries of knowledge space in the online knowledge market?," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-12, December.
- Sick, Nathalie & Bröring, Stefanie, 2022. "Exploring the research landscape of convergence from a TIM perspective: A review and research agenda," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
- Zhaobin Liu & Yongxiang Zhang & Weiwei Deng & Jian Ma & Xia Fan, 2024. "A deep learning method for recommending university patents to industrial clusters by common technological needs mining," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(6), pages 3089-3113, June.
- Seo, Wonchul & Afifuddin, Mokh, 2024. "Developing a supervised learning model for anticipating potential technology convergence between technology topics," Technological Forecasting and Social Change, Elsevier, vol. 203(C).
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More about this item
Keywords
Deep representation learning; Recommendation system; Technology convergence; Technology opportunity discovery; Self-supervised learning;All these keywords.
JEL classification:
- C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
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