Recommending investors for new startups by integrating network diffusion and investors' domain preference

TL;DR

This paper proposes a data-driven method combining network diffusion and investors' domain preferences to improve investor recommendations for new startups, addressing the cold-start problem in venture investment matching.

Contribution

It introduces a novel tripartite network model with virtual links to incorporate domain preferences, enhancing recommendation accuracy for startups with no prior investments.

Findings

Diffusion-based algorithms effectively identify prospective investors.

Incorporating domain preferences improves recommendation performance.

Network representations with virtual links outperform traditional models.

Abstract

Over the past decade, many startups have sprung up, which create a huge demand for financial support from venture investors. However, due to the information asymmetry between investors and companies, the financing process is usually challenging and time-consuming, especially for the startups that have not yet obtained any investment. Because of this, effective data-driven techniques to automatically match startups with potentially relevant investors would be highly desirable. Here, we analyze 34,469 valid investment events collected from www.itjuzi.com and consider the cold-start problem of recommending investors for new startups. We address this problem by constructing different tripartite network representations of the data where nodes represent investors, companies, and companies' domains. First, we find that investors have strong domain preferences when investing, which motivates us to introduce virtual links between investors and investment domains in the tripartite network construction. Our analysis of the recommendation performance of diffusion-based algorithms applied to various network representations indicates that prospective investors for new startups are effectively revealed by integrating network diffusion processes with investors' domain preference.