论文标题
在众筹环境中使用客户选择建模的分类优化
Assortment Optimization with Customer Choice Modeling in a Crowdfunding Setting
论文作者
论文摘要
众筹是从大量人的贡献中筹集资金的行为，是经济理论中最受欢迎的研究主题之一。由于众筹平台（CFP）通过提供多个功能来促进筹集资金的过程，因此我们应该考虑它们在市场上的存在和生存。在这项研究中，我们研究了平台特征在客户行为选择模型中的重要作用。特别是，我们提出了一个多项式logit模型，以在众筹设置中描述客户的（支持者'）行为。我们通过讨论这些平台中的收入分享模型来进行。为此，我们得出结论，分类优化问题可能至关重要，以最大程度地提高平台的收入。在某些情况下，我们能够得出合理数量的数据，并实施两种众所周知的机器学习方法，例如多元回归和分类问题，以预测平台可以为每个到达客户提供的最佳分类。我们比较了这两种方法的结果，并研究了它们在所有情况下的性能。
Crowdfunding, which is the act of raising funds from a large number of people's contributions, is among the most popular research topics in economic theory. Due to the fact that crowdfunding platforms (CFPs) have facilitated the process of raising funds by offering several features, we should take their existence and survival in the marketplace into account. In this study, we investigated the significant role of platform features in a customer behavioral choice model. In particular, we proposed a multinomial logit model to describe the customers' (backers') behavior in a crowdfunding setting. We proceed by discussing the revenue-sharing model in these platforms. For this purpose, we conclude that an assortment optimization problem could be of major importance in order to maximize the platforms' revenue. We were able to derive a reasonable amount of data in some cases and implement two well-known machine learning methods such as multivariate regression and classification problems to predict the best assortments the platform could offer to every arriving customer. We compared the results of these two methods and investigated how well they perform in all cases.
