SocioLink: Leveraging Relational Information in Knowledge Graphs for Startup Recommendations

• 成果类型: Article
• 署名作者: Xu, Ruiyun; Chen, Hailiang; Zhao, J. Leon
• 署名单位: University System of Ohio; Miami University; University of Hong Kong; The Chinese University of Hong Kong, Shenzhen; The Chinese University of Hong Kong, Shenzhen; Shenzhen Research Institute of Big Data
• 刊物名称: JOURNAL OF MANAGEMENT INFORMATION SYSTEMS
• ISSN/ISSBN: 0742-1222
• DOI: 10.1080/07421222.2023.2196771
• 发表日期: 2023
• 页码: 655-682
• 关键词: design science research; Venture capitalists; systems; FinTech; network; MODEL; INNOVATION

摘要

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.