7月13日,罗学明教授(美国天普大学)
发布时间:2018-07-11 发布人:fyy 点击数:427
  

 

博士论坛(57)

 主题:Popularity-based or A.I. Personalization-based Algorithms for the Sharing Economy Platform?(共享经济平台) Evidence from Natural Experimentation and Machine Learning(机器学习)

 

报告人:罗学明教授

时间:7月13日(星期五)10:00

地点:宁远楼424教室

 

讲座人简介:罗学明教授现任美国天普大学教授,查尔斯吉利兰市场营销杰出教授,主席。同时他也是天普大学福克斯商学院移动分析大数据全球中心创始人兼主任。研究内容主要集中在移动消费者行为、大数据营销策略、机器学习和消费者分析等方面。

Abstract:This paper examines how platform recommendation algorithms based on demand-side tastes affect supply-side small scale entrepreneurs. It addresses three vital questions: (1) What is the effect of implementations of popularity or AI personalization platform recommendation algorithms on seller revenue? (2) How do changes of platform algorithms incentivize sellers in the sharing economy? (3) For which sellers the platform algorithms are more beneficial? Through natural quasi-experiments and rich proprietary datasets from a major food-sharing platform, the analysis finds significant increases of seller revenues after the platform implements either algorithm. But, the pathways to these revenue increases differ. As the review popularity recommendation (RPR) platform algorithm helps buyers to find sellers with high review ratings more easily, sellers are incentivized to adopt a specialization focus on the quality reputation of current products. By contrast, as the AI bolter personalization recommendation (BPR) algorithm enables buyers to find sellers with more customized cuisines, sellers respond by adopting an innovation focus on introducing more new products to suit the diverse customer tastes. Consistent with the specialization pathway, RPR is more beneficial for sellers who have a concentrated product assortment. In contrast and in line with the innovation pathway, it is younger and newer entrepreneurs that reap more benefits from the BPR. Surprisingly, each algorithm has unintended outcomes: RPR impedes innovation and BPR inhibits specialization. However, the platform manager can leverage a machine learning causal forest technique to learn sellers’ heterogeneous responses to RPR and BPR and craft an optimal targeting rule, which maximizes algorithms’ benefits and minimizes their negative effects for the sharing platform.


 

 
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