Name
Title | ||
|---|---|---|
Estimating the Days to Success of Campaigns in Crowdfunding: A Deep Survival Perspective |
Abstract | ||
|---|---|---|
Crowdfunding is an emerging mechanism for entrepreneurs or individuals to solicit funding from the public for their creative ideas. However, in these platforms, quite a large proportion of campaigns (projects) fail to raise enough money of backers' supports by the declared expiration date. Actually, it is very urgent to predict the exact success time of campaigns. But this problem has not been well explored due to a series of domain and technical challenges. In this paper, we notice the implicit factor of distribution of backing behaviors has a positive impact on estimating the success time of the campaign. Therefore, we present a focused study on predicting two specific tasks, i.e., backing distribution prediction and success time prediction of campaigns. Specifically, we propose a Seq2seq based model with Multi-facet Priors (SMP), which can integrate heterogeneous features to jointly model the backing distribution and success time. Additionally, to keep the change of backing distributions more smooth as the backing behaviors increases, we develop a linear evolutionary prior for backing distribution prediction. Furthermore, due to high failure rate, the success time of most campaigns is unobservable. We model this censoring phenomenon from the survival analysis perspective and also develop a non-increasing prior and a partial prior for success time prediction. Finally, we conduct extensive experiments on a real-world dataset from Indiegogo. Experimental results clearly validate the effectiveness of SMP. |
Year | Venue | Field |
|---|---|---|
2019 | THIRTY-THIRD AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE / THIRTY-FIRST INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE / NINTH AAAI SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE | Econometrics,Computer science,Failure rate,Artificial intelligence,Notice,Expiration date,Prior probability,Censoring (statistics),Unobservable,Machine learning |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Binbin Jin | 1 | 6 | 2.48 |
| Hongke Zhao | 2 | 82 | 9.66 |
| Enhong Chen | 3 | 2106 | 165.57 |
| Liu Qi | 4 | 1027 | 106.48 |
| Yong Ge | 5 | 1205 | 74.10 |