SocioLink: Leveraging Relational Information in Knowledge Graphs for Startup Recommendations

Journal of Management Information Systems, Forthcoming

45 Pages Posted: 28 Sep 2022

See all articles by Ruiyun Rayna Xu

Ruiyun Rayna Xu

The Chinese University of Hong Kong, Shenzhen - Shenzhen Research Institute of Big Data

Hailiang Chen

HKU Business School, The University of Hong Kong

J. Leon Zhao

Chinese University of Hong Kong, Shenzhen

Date Written: September 2022

Abstract

While venture capital firms are increasingly relying on recommendation models in investment decisions, existing startup recommendation models fail to consider the uniqueness of venture capital context, including two-sided matching between investing and investee firms and a lack of information disclosure requirements on startups. Following the design science research paradigm and guided by the proximity principle from social psychology, we develop a novel framework called SocioLink by depicting and analyzing various relations in a knowledge graph via machine learning. Our experimental results show that SocioLink significantly outperforms state-of-the-art startup recommendation methods in both accuracy and quality. This improvement is driven by not only the inclusion of social relations but also the superiority of modelling relations via knowledge graph. We also develop a web-based prototype to demonstrate explainable artificial intelligence. This work contributes to the FinTech literature by adding an innovative design artifact—SocioLink—for decision support in the investment context.

Keywords: design science, FinTech, knowledge graph, startup recommendations, machine learning

Suggested Citation

Xu, Ruiyun and Chen, Hailiang and Zhao, J. Leon, SocioLink: Leveraging Relational Information in Knowledge Graphs for Startup Recommendations (September 2022). Journal of Management Information Systems, Forthcoming, Available at SSRN: https://ssrn.com/abstract=4217147

Ruiyun Xu

The Chinese University of Hong Kong, Shenzhen - Shenzhen Research Institute of Big Data ( email )

Hailiang Chen (Contact Author)

HKU Business School, The University of Hong Kong ( email )

K. K. Leung Building
University of Hong Kong
Hong Kong

HOME PAGE: http://www.fbe.hku.hk/people/academic/hailiang-chen

J. Leon Zhao

Chinese University of Hong Kong, Shenzhen ( email )

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