Privacy-Preserving Personalized Recommender Systems
Prof. Xingyu FU
Lecturer (Assistant Professor), School of Marketing, UNSW Business School, Sydney
Date: 7 November 2024 (Thursday)
Time: 14:30 – 16:00
Venue: E22-G004
Host: Prof. Yingpeng ZHU, Assistant Professor in Business Intelligence and Analytics
Abstract
Problem Definition: Personalized product recommendations are crucial for online platforms but pose privacy risks due to potential inference attacks. To address these concerns, we propose recommendation policies that adhere to differential privacy constraints.
Methodology and Results: We develop a theoretical model where the recommendation policy selects products based on consumers’ preference rankings, learned from personal data. Unlike conventional recommendation policies that primarily focus on prospering from meeting consumer satisfaction, our approach applies differential privacy to mitigate the risk of exposing personal information to man-in-the-middle attackers during the transmission of recommendations over communication networks, such as the Internet. As a result, this policy accounts for the trade-off between personalization and privacy. Our analysis shows that the optimal policy is a coarse-grained threshold policy, where products are randomly recommended with either high or low probability based on whether their preference ranks are above or below a certain threshold. We further explore the comparative statics of this threshold in an asymptotic regime with a large number of products, as is typical for online platforms. Moreover, we examine the economic implications of privacy protection. When product prices are exogenous, privacy protection reduces consumer surplus due to lower match values between consumers and recommended products. However, when retailers set prices endogenously, the impact on consumer surplus is non-monotonic, reflecting a trade-off between recommendation accuracy and price inflation.
Managerial and Regulatory Implications: Our findings offer valuable insights for practitioners developing privacy-preserving personalized recommendation policies and provide regulators with a deeper understanding of the economic consequences of privacy protection in recommender systems.
Speaker
Xingyu Fu is a Lecturer (Assistant Professor) at the School of Marketing, UNSW Business School. He holds a PhD from HKUST. His research interests include socially responsible/sustainable operations, the marketing-operations interface, and the economics of AI. His research has been published/under revision in journals such as Manufacturing & Service Operations Management, Naval Research Logistics, and Service Science.
All are welcome!
