Project Success Prediction in Crowdfunding Environments
Project Success Prediction in Crowdfunding Environments
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DOI：
10.1145/2835776.2835791
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发表时间：
2016-02
期刊：
影响因子：
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通讯作者：
Yan Li;Vineeth Rakesh;Chandan K. Reddy
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文献类型：
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作者：
Yan Li;Vineeth Rakesh;Chandan K. Reddy
众筹近年来受到广泛关注。尽管众筹平台取得了巨大的成功，但成功实现预期目标的项目比例仅为40%左右。此外，许多众筹平台遵循“全有或全无”的政策，这意味着只有在预定义的时间内达到目标时才能收取承诺金额。因此，估算项目成功的概率是众筹领域最重要的研究挑战之一。为了预测项目的成功，需要新的预测模型，可以潜在联合收割机分类（包括成功和失败的项目）和回归（用于估计成功的时间）的能力。在本文中，我们制定了项目的成功预测作为一个生存分析问题，并应用删失回归方法，其中一个可以进行回归部分信息的存在。我们严格研究了众筹数据的项目成功时间分布，并表明逻辑和对数逻辑分布是从这些数据中学习的自然选择。我们使用18K Kickstarter（一个流行的众筹平台）项目的综合数据和从Twitter收集的116K相应推文来研究各种删失回归模型。我们发现，在训练阶段充分利用成功和失败项目的模型在预测未来项目的成功方面比只使用成功项目的模型表现得更好。我们提供了一个严格的评估，许多套相关的功能，并表明，增加一些时间的功能，在项目的早期阶段获得可以显着提高性能。
Crowdfunding has gained widespread attention in recent years. Despite the huge success of crowdfunding platforms, the percentage of projects that succeed in achieving their desired goal amount is only around 40%. Moreover, many of these crowdfunding platforms follow "all-or-nothing" policy which means the pledged amount is collected only if the goal is reached within a certain predefined time duration. Hence, estimating the probability of success for a project is one of the most important research challenges in the crowdfunding domain. To predict the project success, there is a need for new prediction models that can potentially combine the power of both classification (which incorporate both successful and failed projects) and regression (for estimating the time for success). In this paper, we formulate the project success prediction as a survival analysis problem and apply the censored regression approach where one can perform regression in the presence of partial information. We rigorously study the project success time distribution of crowdfunding data and show that the logistic and log-logistic distributions are a natural choice for learning from such data. We investigate various censored regression models using comprehensive data of 18K Kickstarter (a popular crowdfunding platform) projects and 116K corresponding tweets collected from Twitter. We show that the models that take complete advantage of both the successful and failed projects during the training phase will perform significantly better at predicting the success of future projects compared to the ones that only use the successful projects. We provide a rigorous evaluation on many sets of relevant features and show that adding few temporal features that are obtained at the project's early stages can dramatically improve the performance.