Estimating Early Fundraising Performance of Innovations via Graph-based Market Environment Model

TL;DR

This paper introduces a graph-based neural network model to predict early crowdfunding success by analyzing market environment factors, addressing a previously under-explored challenge.

Contribution

The paper presents a novel graph-based market environment model that jointly captures market competition and evolution for early fundraising performance estimation.

Findings

Effective in predicting early crowdfunding success

Outperforms baseline models on real-world data

Demonstrates the importance of market environment modeling

Abstract

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.