Crosscutting Areas No-Regret Bayesian Recommendation to Homogeneous Users

• 成果类型: Article
• 署名作者: Feng, Yiding; Tang, Wei; Xu, Haifeng
• 署名单位: Hong Kong University of Science & Technology; Chinese University of Hong Kong; University of Chicago
• 刊物名称: OPERATIONS RESEARCH
• ISSN/ISSBN: 0030-364X
• DOI: 10.1287/opre.2023.0556
• 发表日期: 2026
• 关键词: Online learning; Bayesian recommendation; Regret minimization; linear program; persuasion

摘要

We introduce and study the online Bayesian recommendation problem for a recommender system platform. The platform has the privilege to privately observe a utilityrelevant state of a product at each round and uses this information to make online recommendations to a stream of myopic users. This paradigm is common in a wide range of scenarios in the current internet economy. The platform commits to an online recommendation policy that utilizes its information advantage on the product state to persuade self-interested users to follow the recommendation. Because the platform does not know users' preferences or beliefs in advance, we study the platform's online learning problem of designing an adaptive recommendation policy to persuade users while gradually learning users' preferences and beliefs en route. Specifically, we aim to design online learning policies with no Stackelberg regret for the platform, that is, against the optimal benchmark policy in hindsight under the assumption that users will correspondingly adapt their responses to the benchmark policy. Our first result is an online policy that achieves double logarithmic regret dependence on the number of rounds. We also present an information-theoretic lower bound showing that no adaptive online policy can achieve regret with better dependency on the number of rounds. Finally, by formulating the platform's problem as optimizing a linear program with membership oracle access, we present our second online recommendation policy that achieves regret with polynomial dependence on the number of states but logarithmic dependence on the number of rounds.