Recommendation Mechanism for Patent Trading Empowered by Heterogeneous Information Networks

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.

KEY WORDS AND PHRASES:

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

Acknowledgments

Qi Wang and Wei Du contributed equally to this paper. We gratefully thank the Editor-in-Chief and all reviewers. We also acknowledge with gratitude the generous support of the National Natural Science Foundation of China (M155200003, 71371164, 91546119), CityU Research Grant (9620365, 7004715, 7004528, 9680121), Renmin University of China (18XNLG03), and the Ministry of Education (18YJC630025).

Notes

1. http://www.patentsview.org/web/

2. https://www.uspto.gov/learning-and-resources/electronic-data-products/patent-assignment-dataset

Additional information

Notes on contributors

Qi Wang

QI WANG ([email protected]) is a joint Ph.D. student of City University of Hong Kong and Xi’an Jiaotong University, China. She received a bachelor’s degree from the Business School, Jilin University. Her research interests concern university–industry collaboration and recommendation systems. Her research has been published in Decision Support Systems and presented at the Hawaii International Conference on System Sciences.

Wei Du

WEI DU ([email protected]; corresponding author) is a lecturer in School of Information, Renmin University, China. She received her doctorate from City University of Hong Kong. Her research interests include recommender systems, social network analysis, and knowledge organization systems. Her research has been published in Scientometrics, the proceedings of International Conference on Information Systems and Hawaii International Conference on System Sciences, and other venues.

Jian Ma

JIAN MA ([email protected]) is a professor in the Department of Information Systems, City University of Hong Kong. He received his Doctor of Engineering degree in Computer Science from Asia Institute of Technology. Dr. Ma’s research areas include decision and decision support systems, business intelligence, research information systems, and social networks for research and innovation. His work has been published in IEEE Transactions on Engineering Management, IEEE Transactions on Systems, Man and Cybernetics, Decision Support Systems, Information and Management, European Journal of Operational Research, Scientometrics, and other venues.

Xiuwu Liao

XIUWU LIAO ([email protected]) is a professor in the School of Management, Xi’an Jiaotong University, China. He received his doctorate from Dalian University of Technology, China. Dr. Liao’s research covers multicriteria decision making, could service pricing, social commerce, online reviews, and IT outsourcing. He has published in Information Systems Research, Annals of Operations Research, Decision Support Systems, Information Systems, Knowledge-based Systems, Omega, and European Journal of Operational Research.