Privacy-Preserving Personalized Recommender Systems

Problem definition: Personalized product recommendations are crucial for online platforms but pose privacy risks. To address these concerns, we propose recommendation policies that adhere to differential privacy constraints. Methodology/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 tradeoff between personalization and privacy. Our analysis shows 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 rankings 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 nonmonotonic, reflecting a tradeoff between recommendation accuracy and price inflation. Managerial implications: Our findings offer 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. Funding: X. Fu acknowledges financial support from the University of New South Wales [Start-Up Grant, UNSW Business School Dean’s Research Fellowship]. N. Chen is supported by the Institute for Management & Innovation (IMI) Research Grant. P. Gao’s research is supported by the National Natural Science Foundation of China [Grants 72522026, 72201234 and 72192805], Collaborative Research Funding Hong Kong [Grant C6032-21G], and the Guangdong Provincial Key Laboratory of Mathematical Foundations for Artificial Intelligence [Grant 2023B1212010001]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.0271 .

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