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EAGER: An Integrated Predictive Modeling Framework for Crowdfunding Environments

EAGER：众筹环境的集成预测建模框架

基本信息

批准号：
1646881
负责人：
Chandan Reddy
金额：
$ 9.99万
依托单位：
Virginia Polytechnic Institute and State University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2016
资助国家：
美国
起止时间：
2016-08-15 至 2018-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1646881&HistoricalAwards=false
关键词：
EAGER Integrated Predictive Modeling Framework

项目摘要

The research aims to study data analytics tools for improving crowdfunding project success rate. Crowdfunding provides seed capital for start-up companies, creating job opportunities and reviving lost business ventures. In spite of the widespread popularity and innovativeness in the concept of crowdfunding, however, many projects are still not able to succeed. A deeper understanding of the factors affecting investment decisions will not only give better success rate to the future projects but will also provide appropriate guidelines for project creators who will be seeking funding. The crowdfunding domain poses several new challenges from the data analytics perspective due to the heterogeneous, complex and dynamic nature of the data associated with project campaigns. This project develops a systematic data-driven approach to resolve these challenges by utilizing vast amounts of historical data which can be leveraged to accurately predict the success of crowdfunding projects. Though the proposed methods are primarily developed in the context of crowdfunding, they are applicable to various other forms of social data that will be collected in other disciplines such as social science, engineering, and finance.This project develops an integrated predictive modeling framework to solve some of the complex underlying problems related to bringing success to crowdfunding based projects. Existing approaches in data analytics for classification and regression cannot tackle this project success prediction problem since the goal is to estimate the time for a project to reach its success. The research team develops a unified probabilistic prediction framework which simultaneously integrates classification and regression together. In addition, a novel iterative imputation mechanism, which calibrates the time to project success, is proposed for reducing the bias in the model estimators. This project can demonstrate the power of data analytics in delivering better insights about various categories of real-world projects by not only accurately estimating the chances of being successful but also quantitatively assessing the factors that are responsible for bringing success in crowdfunding environments. The progress of the project and the research findings are disseminated via the project website (http://dmkd.cs.vt.edu/projects/crowdfunding/).

本研究旨在研究提高众筹项目成功率的数据分析工具。众筹为初创公司提供种子资金，创造就业机会，重振失败的商业企业。尽管众筹的概念广受欢迎并具有创新性，但仍有许多项目未能成功。更深入地了解影响投资决策的因素，不仅将提高未来项目的成功率，还将为将寻求资金的项目创建者提供适当的指导。由于与项目活动相关的数据的异质性、复杂性和动态性，从数据分析的角度来看，众筹领域提出了若干新的挑战。该项目开发了一种系统的数据驱动的方法，通过利用大量的历史数据来解决这些挑战，这些数据可以被用来准确预测众筹项目的成功。虽然建议的方法主要是在众筹的背景下开发的，但它们也适用于将在社会科学、工程学和金融学等其他学科收集的各种其他形式的社会数据。该项目开发了一个集成的预测建模框架，以解决与众筹项目成功相关的一些复杂的潜在问题。现有的用于分类和回归的数据分析方法不能解决这个项目成功预测问题，因为其目标是估计项目达到其成功的时间。研究小组开发了一个统一的概率预测框架，同时将分类和回归结合在一起。此外，为了减少模型估计器的偏差，提出了一种新的迭代补偿机制，该机制校准项目成功所需的时间。这个项目可以展示数据分析的力量，通过不仅准确地估计成功的机会，而且定量地评估在众筹环境中带来成功的因素，来更好地洞察各种类型的现实世界项目。项目的进展和研究结果通过项目网站(http://dmkd.cs.vt.edu/projects/crowdfunding/).发布。

项目成果

期刊论文数量（14）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

How Fast Will You Get a Response? Predicting Interval Time for Reciprocal Link Creation

DOI：
10.1609/icwsm.v11i1.14961
发表时间：
2017-05
期刊：
Physical Review E
影响因子：
2.4
作者：
Vachik S. Dave;M. Hasan;Chandan K. Reddy
通讯作者：
Vachik S. Dave;M. Hasan;Chandan K. Reddy

Probabilistic Social Sequential Model for Tour Recommendation

DOI：
10.1145/3018661.3018711
发表时间：
2017-02
期刊：
Proceedings of the Tenth ACM International Conference on Web Search and Data Mining
影响因子：
0
作者：
Vineeth Rakesh;Niranjan Jadhav;Alexander Kotov;Chandan K. Reddy
通讯作者：
Vineeth Rakesh;Niranjan Jadhav;Alexander Kotov;Chandan K. Reddy

Localized user-driven topic discovery via boosted ensemble of nonnegative matrix factorization