消费者选择的社交大数据分析：一个双向在线平台的视角
Social Big Data Analytics of Consumer Choices: A Two Sided Online Platform Perspective
摘要：
本论文探讨了三个不同的大数据分析相关的消费者选择的社会方面的问题。这一系列研究的主要目标是帮助双边平台公司在客户异质性很大的情况下制定营销策略。在三篇文章中，我结合了结构建模和机器学习方法，首先使用我从网络上收集的独特数据集来了解客户对内在和外在因素的反应，然后探索优化双边平台公司反应的方法。第一篇文章考察了移动应用商店背景下的“社会学习”，控制了享乐主义和功利主义移动应用程序的内在价值、价格、广告和可用选项的数量。第二篇论文以eBay网上拍卖平台为背景，探讨了竞拍者的预期赢家和输家后悔。本文利用eBay的大数据集和经验贝叶斯估计方法，量化了不同产品类别下投标人的后悔预期，并研究了经验在解释投标人后悔和学习行为中的作用。第三篇论文研究了用户生成内容的在线平台中游戏化激励机制的效果。我使用LDA集成方法，混合正态和k均值聚类方法将用户划分为竞争者，合作者，成就者，探索者和不感兴趣的用户。这些发现有助于游戏化平台瞄准其用户。模拟反事实分析表明，双边平台可以通过使赚取徽章更加困难来增加用户贡献的数量。
英文摘要：
This dissertation examines three distinct big data analytics problems related to the social aspects of consumers' choices. The main goal of this line of research is to help two sided platform firms to target their marketing policies given the great heterogeneity among their customers. In three essays, I combined structural modeling and machine learning approaches to first understand customers' responses to intrinsic and extrinsic factors, using unique data sets I scraped from the web, and then explore methods to optimize two sided platforms' firms' reactions accordingly. The first essay examines "social learning" in the mobile app store context, controlling for intrinsic value of hedonic and utilitarian mobile apps, price, advertising, and number of options available. The second essay investigates bidders' anticipated winner and loser regret in the context of the eBay online auction platform. Using a large data set from eBay and empirical Bayesian estimation method, I quantify the bidders' anticipation of regret in various product categories, and investigate the role of experience in explaining the bidders' regret and learning behaviors. The third essay investigates the effects of Gamification incentive mechanisms in an online platform for user generated content. I use an ensemble method over LDA, mixed normal and k-mean clustering methods to segment users into competitors, collaborators, achievers, explorers and uninterested users. These findings help the Gamification platform to target its users. The simulation counterfactual analysis suggests that a two sided platform can increase the number of user contributions, by making earning badges more difficult.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
