题　目：Recommender Systems under Privacy Protection

嘉　宾：汪寿强，副教授，德克萨斯大学达拉斯分校

主持人：江浦平 助理教授 77779193永利官网

时　间：2024年7月16日（周二）10:00-11:30am

地　点：安泰楼B207室

内容简介：

Consumers make inferences about a product’s relevance or even get access to it through recommendations. Namely, recommendations often play both informative and allocative roles. However, the pervasive use of personal data by modern algorithmic recommender systems has sparked public outcry for tighter privacy regulations. Personal preferences over different product offerings are a basic constituent of consumer privacy. We study how a profit-driven online platform designs its recommender policies in response to different privacy protection regimes that grant users varying degrees of control over their personal data. We demonstrate the effective equivalence between the opt-out protection and the unprotected privacy. As a key finding, consumers’ autonomy over their privacy (to the extent that they could misrepresent their personal data) may compel platforms to distort their recommender policies and lead to unintended consequences. When the recommendation only plays an informative role, such level of privacy protection deters the platform from any personalized recommendation; if the recommendation can, in addition, act allocatively to control consumers’ access to products, algorithmic discrimination may arise, whereby the disadvantaged minority in the society are restricted or deprived of access to potential valuable opportunities. Ultimately, these distortions could inadvertently hurt both platforms and consumers, relative to less stringent privacy protection regimes. Counter-intuitively, enacting the recommendation’s allocative role (by restricting users’ access to certain products) in addition to its informative role can in fact benefit both the platform as well as the users, especially when users are given the autonomy over their privacy.

演讲人简介：

Shouqiang Wang is currently an Associate Professor of Operations Management (with tenure) at the Naveen Jindal School of Management, University of Texas at Dallas. His research focuses on strategic operations problems that arise from both business settings as well as public domains, with a particular interest in incentive issues in the presence of asymmetric information and dynamic interactions among decentralized stakeholders in these contexts. His research has been published at premier business research journals such as Management Science, Operations Research, and Manufacturing & Service Operations Management, for which he also serves as Associate Editors. His works and those of his doctoral students have received multiple best paper awards. He teaches statistics, business analytics, spreadsheet modeling, operations management, logistics, and procurement at undergraduate, graduate, and MBA levels. He received his Bachelors in mathematics and economics from Peking University, his Master in statistics and PhD in business administration from Duke University.

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