Estimating Early Fundraising Performance of Innovations via Graph-based Market Environment Model
This addresses a challenging and under-explored issue for creators and platforms in the crowdfunding market, though it appears incremental in applying graph-based methods to this domain.
The paper tackles the problem of estimating early fundraising performance for crowdfunding projects before they are published, proposing a Graph-based Market Environment model (GME) that leverages market competition and evolution, with experiments on Indiegogo data showing its effectiveness.
Well begun is half done. In the crowdfunding market, the early fundraising performance of the project is a concerned issue for both creators and platforms. However, estimating the early fundraising performance before the project published is very challenging and still under-explored. To that end, in this paper, we present a focused study on this important problem in a market modeling view. Specifically, we propose a Graph-based Market Environment model (GME) for estimating the early fundraising performance of the target project by exploiting the market environment. In addition, we discriminatively model the market competition and market evolution by designing two graph-based neural network architectures and incorporating them into the joint optimization stage. Finally, we conduct extensive experiments on the real-world crowdfunding data collected from Indiegogo.com. The experimental results clearly demonstrate the effectiveness of our proposed model for modeling and estimating the early fundraising performance of the target project.