Recommending investors for crowdfunding projects

• Main Authors: AN, Jisun, QUERCIA, Daniele, CROWCROFT, Jon
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2014
• Subjects: crowdfunding; Kickstarter; recommending systems; Twitter; Artificial Intelligence and Robotics; Numerical Analysis and Scientific Computing
• Online Access: https://ink.library.smu.edu.sg/sis_research/6285; https://ink.library.smu.edu.sg/context/sis_research/article/7288/viewcontent/Recommending_Investors_for_Crowdfunding_Projects.pdf
• Institution: Singapore Management University

To bring their innovative ideas to market, those embarking in new ventures have to raise money, and, to do so, they have often resorted to banks and venture capitalists. Nowadays, they have an additional option: that of crowdfunding. The name refers to the idea that funds come from a network of people on the Internet who are passionate about supporting others' projects. One of the most popular crowdfunding sites is Kickstarter. In it, creators post descriptions of their projects and advertise them on social media sites (mainly Twitter), while investors look for projects to support. The most common reason for project failure is the inability of founders to connect with a sufficient number of investors, and that is mainly because hitherto there has not been any automatic way of matching creators and investors. We thus set out to propose different ways of recommending investors found on Twitter for specific Kickstarter projects. We do so by conducting hypothesis-driven analyses of pledging behavior and translate the corresponding findings into different recommendation strategies. The best strategy achieves, on average, 84% of accuracy in predicting a list of potential investors' Twitter accounts for any given project. Our findings also produced key insights about the whys and wherefores of investors deciding to support innovative efforts.