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Long-term study of crowdfunding plat...

Chung, Jinwook.

Long-term study of crowdfunding platform: Predicting project success and fundraising amount.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Long-term study of crowdfunding platform: Predicting project success and fundraising amount.
作者:	Chung, Jinwook.
面頁冊數:	46 p.
附註:	Source: Masters Abstracts International, Volume: 54-05.
附註:	Adviser: Kyumin Lee.
Contained By:	Masters Abstracts International54-05(E).
標題:	Computer science.
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1591648
ISBN:	9781321834710

Long-term study of crowdfunding platform: Predicting project success and fundraising amount.
Chung, Jinwook.

Long-term study of crowdfunding platform: Predicting project success and fundraising amount. - 46 p.

Source: Masters Abstracts International, Volume: 54-05.

Thesis (M.S.)--Utah State University, 2015.

Crowdfunding platforms have become important sites where people can create projects to seek funds toward turning their ideas into products, and back someone else's projects. As news media have reported successfully funded projects (e.g., Pebble Time, Coolest Cooler), more people have joined crowdfunding platforms and launched projects. But in spite of rapid growth of the number of users and projects, a project success rate at large has been decreasing because of launching projects without enough preparation and experience.

ISBN: 9781321834710Subjects--Topical Terms:

199325
Computer science.

Long-term study of crowdfunding platform: Predicting project success and fundraising amount.
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520 $a To solve the problem, in this thesis we (i) collect the largest datasets from Kickstarter, consisting of all project profiles, corresponding user profiles, projects' temporal data and users' social media information; (ii) analyze characteristics of successful projects, behaviors of users and understand dynamics of the crowdfunding platform; (iii) propose novel sta- tistical approaches to predict whether a project will be successful and a range of expected pledged money of the project; and (iv) develop predictive models and evaluate performance of the models. Our experimental results show that the predictive models can effectively predict project success and a range of expected pledged money.
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