  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.
