Hongke Zhao (USTC);Hefu Zhang (University of Science and Technology of China);Yong Ge (The University of Arizona);Qi Liu (USTC);Enhong Chen (University of Science & Technology of China);Huayu Li (UNCC);Le Wu (HeFei University of Technology)
Crowdfunding is an emerging Internet fundraising mechanism by raising monetary contributions from the crowd for projects or ventures. In these platforms, the dynamics, i.e., daily funding amount on campaigns and perks (backing options with rewards), are the most concerned issue for creators, backers and platforms. However, tracking the dynamics in crowdfunding is very challenging and still underexplored. To that end, in this paper, we present a focused study on this problem. A special goal is to forecast the funding amount in the future days for a given campaign and its perks. Specifically, we formalize the dynamics in crowdfunding as a hierarchical time series, i.e., campaign level and perk level. Specific to each level, we develop a special regression model by modeling the decision making process of the crowd (visitors and backing probability) and exploring various factors that impact the decision; on this basis, an enhanced switching regression is proposed at each level to address the heterogeneity of funding sequences. Further, we employ a revision matrix to combine the two-level base forecasts for the final forecasting. We conduct extensive experiments on a real-world crowdfunding data collected from Indiegogo. The experimental results clearly demonstrate the effectiveness of our approaches on tracking the dynamics in crowdfunding.