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Type Journal Article
Scope Discipline-based scholarship
Title Recommending investors for new startups by integrating network diffusion and investors’ domain preference
Organization Unit
Authors
  • Shuqi Xu
  • Qianming Zhang
  • Linyuan Lü
  • Manuel Mariani
Item Subtype Original Work
Refereed Yes
Status Published in final form
Language
  • English
Journal Title Information Sciences
Publisher Elsevier
Geographical Reach international
ISSN 0020-0255
Volume 515
Page Range 103 - 115
Date 2020
Abstract Text 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.
Digital Object Identifier 10.1016/j.ins.2019.11.045
Other Identification Number merlin-id:18862
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