Header

UZH-Logo

Maintenance Infos

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


Xu, Shuqi; Zhang, Qianming; Lü, Linyuan; Mariani, Manuel (2020). Recommending investors for new startups by integrating network diffusion and investors’ domain preference. Information Sciences, 515:103-115.

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.

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.

Statistics

Citations

Altmetrics

Downloads

2 downloads since deposited on 09 Jan 2020
2 downloads since 12 months
Detailed statistics

Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:03 Faculty of Economics > Department of Business Administration
08 Research Priority Programs > Social Networks
Dewey Decimal Classification:330 Economics
Language:English
Date:1 April 2020
Deposited On:09 Jan 2020 16:47
Last Modified:09 Jan 2020 17:16
Publisher:Elsevier
ISSN:0020-0255
OA Status:Closed
Publisher DOI:https://doi.org/10.1016/j.ins.2019.11.045
Other Identification Number:merlin-id:18862

Download

Closed Access: Download allowed only for UZH members