Dynamic Online Recommendation for Two-Sided Market with Bayesian Incentive Compatibility

TL;DR

This paper introduces a dynamic recommendation protocol that balances exploration and exploitation while ensuring incentive compatibility, validated through theoretical guarantees and real-world experiments.

Contribution

It formalizes the challenge of incentive-compatible online recommendations and proposes a novel two-stage algorithm with proven regret bounds and incentive guarantees.

Findings

Achieves $O( oot{K}{d}T^{1/2})$ regret bound.

Ensures Bayesian incentive compatibility under Gaussian priors.

Demonstrates strong incentive and regret performance in simulations and real-world data.

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) dynamic incentive compatibility in accounting for users' self-interested behaviors and heterogeneous preferences. This paper formalizes these challenges into a Dynamic Bayesian Incentive-Compatible Recommendation Protocol (DBICRP). To address the DBICRP, 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 dynamic incentive compatibility to determine sufficient sample sizes. The second stage employs inverse proportional gap sampling integrated with an arbitrary machine learning method to ensure sublinear regret. Theoretically, we prove that RCB achieves $O(\sqrt{KdT})$ regret and satisfies Bayesian incentive compatibility (BIC) under a Gaussian prior assumption. 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.