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

arXiv: https://arxiv.org/abs/1912.06767 · HTML: https://mlsanity.com/paper/1912.06767/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Dec 2019.

## Built on by (most cited first, 4 of 4)

- [Learning the Implicit Semantic Representation on Graph-Structured Data](https://mlsanity.com/paper/2101.06471/) (2021)
- [GUESR: A Global Unsupervised Data-Enhancement with Bucket-Cluster Sampling for Sequential Recommendation](https://mlsanity.com/paper/2303.00243/) (2023)
- [Estimating Fund-Raising Performance for Start-up Projects from a Market Graph Perspective](https://mlsanity.com/paper/2105.12918/) (2021)
- [Recognizing Unseen Objects via Multimodal Intensive Knowledge Graph Propagation](https://mlsanity.com/paper/2306.08487/) (2023)

## Abstract (arXiv)

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.