SocioLink: Leveraging Knowledge Graph for Startup Recommendations in Venture Capital
PI: Dr. Hailiang Chen, HKU
Co-I: Prof. Jianliang Leon Zhao, The Chinese University of Hong Kong, Shenzhen
Investment selection has been a challenging task for venture capitalists (VCs) due to information asymmetry and two-sided matching between VCs and startup companies. Guided with the proximity principle from social psychology and its applications in management and finance, we found that previous efforts in startup recommendation fall short because they did not take full advantage of relation information that can signal the level of trust, cost in private information exchange, and communication effectiveness between two parties. Equipped with this important preliminary finding, we set out to develop a novel framework called SocioLink for startup recommendation. First, a knowledge graph is constructed to describe social connections, geographic proximity, and industry relatedness between venture capitalists and startups. Then, a graph embedding approach and a meta- path-based approach are employed to model the multi-relational information in the graph. We plan to conduct computational experiments to evaluate the proposed recommendation framework. In addition, we also plan to develop a web-based prototype to provide startup recommendations to a given investor and offer explainable intelligence by illustrating the connectivity patterns between the investor and each startup.