Context-Aware Online Recommendation with Bayesian Incentive Compatibility

Yuantong Li, Guang Cheng, Xiaowu Dai

Keywords: recommendation, online learning, incentive compatibility

Abstract:

Recommender systems play a crucial role in internet economies by connecting users with relevant products or services. However, designing effective recommender systems faces two key challenges: (1) the exploration-exploitation tradeoff in balancing new product exploration against exploiting known preferences, and (2) context-aware Bayesian incentive compatibility in accounting for users' heterogeneous preferences and self-interested behaviors. This paper formalizes these challenges into a Context-aware Bayesian Incentive-Compatible Recommendation Problem (CBICRP).
To address the CBICRP, we propose a two-stage algorithm (RCB) that integrates incentivized exploration with an efficient offline learning component for exploitation.
In the first stage, our algorithm explores available products while maintaining context-aware Bayesian incentive compatibility to determine sufficient sample sizes. The second stage employs inverse proportional gap sampling integrated with arbitrary efficient machine learning method to ensure sublinear regret.
Theoretically, we prove that RCB achieves $O(\sqrt{KdT})$ regret and satisfies Bayesian incentive compatibility (BIC). Empirically, we validate RCB's strong incentive gain, sublinear regret, and robustness through simulations and a real-world application on personalized warfarin dosing. Our work provides a principled approach for incentive-aware recommendation in online preference learning settings.

Primary Area: reinforcement learning

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Submission Number: 10962