Recommendation Mechanism for Patent Trading Empowered by Heterogeneous Information Networks

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalNot applicablepeer-review

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Detail(s)

Original languageEnglish
Pages (from-to)147-178
Journal / PublicationInternational Journal of Electronic Commerce
Volume23
Issue number2
Early online date26 Mar 2019
Publication statusPublished - 2019

Abstract

The emerging patent trading platforms help to ease information asymmetry and trust issues during transaction, but a proactive recommendation mechanism that intelligently helps patent buyers identify relevant patents is still absent in the literature. This study proposes a recommendation mechanism for patent trading empowered by heterogeneous information networks (HIN) that integrates various patent information such as patent trading, patent invention, patent citation, patent ontology, and patent contents. Further, the meta-path-based similarity measure (i.e., AvgSim) is employed to calculate relevance and identify the different motivations of potential buyers in buying patents. We conducted two experiments to examine the performance of a proposed mechanism. An offline experiment on Public PatentsView database and Patent Assignment database show that the HIN-empowered recommendation outperforms baseline methods. We also implemented the proposed mechanism on a real-world trading platform (www.InnoCity.com). The recommendation function achieves satisfying results by tracking users’ feedback, which further validates the usability of HIN-empowered recommendation in a patent trading context.

Research Area(s)

  • Heterogeneous information networks, patent recommendation, patent trading, recommendation systems, recommenders

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Research Unit(s) information for this publication is provided by the author(s) concerned.