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Recommendation system for technology convergence opportunities based on self-supervised representation learning

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  • Jungpyo Lee

    (Yonsei University)

  • So Young Sohn

    (Yonsei University)

Abstract

We show how a deep neural network can be designed to learn meaningful representations from high-dimensional and heterogeneous categorical features in patent data using self-supervised learning. Based on each firm’s technology portfolio and each patent’s co-classification information, we propose a novel recommendation system for firms seeking new convergence opportunities through representations of convergence items and firms. The results of this work are expected to recommend convergence opportunities in multiple technology fields by considering the target firm’s potential preference. First, we create a technology portfolio consisting of a set of patents owned by each firm. Then, we train a neural network to extract latent representations of firms and technology convergence items. Despite a lack of indicators related to a firm’s latent preference for a convergence item, a self-supervised neural network can capture the similarity with semantic information of firm’s latent preference that is implicitly present in patent’s co-classification information in each firm’s technology portfolio. We then calculate the similarity between the vector of a target firm and convergence items for recommendation. The top N similar convergence items that have the highest scores are recommended as the new convergence items for the target firm. We apply our framework to the dataset of patents granted by the United States Patent and Trademark Office between 2011 and 2015. The results indicate that the recent development in theories and empirical studies of deep representation learning can shed new light on extracting valuable information from the structured part of patent data.

Suggested Citation

  • Jungpyo Lee & So Young Sohn, 2021. "Recommendation system for technology convergence opportunities based on self-supervised representation learning," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(1), pages 1-25, January.
  • Handle: RePEc:spr:scient:v:126:y:2021:i:1:d:10.1007_s11192-020-03731-y
    DOI: 10.1007/s11192-020-03731-y
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    2. 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).
    3. 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.
    4. 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).
    5. 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.

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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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