AI RESEARCH
Incentivized Exploration with Stochastic Covariates: A Two-Stage Mechanism Design for Recommender System
arXiv CS.LG
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ArXi:2406.04374v2 Announce Type: replace-cross Recommender systems play a crucial role in internet economies by connecting users with relevant products. However, designing effective recommender systems faces the key challenges: the exploration-exploitation tradeoff in securing incentive to explore new products against user's self-interested preferences. While prior work addresses Bayesian Incentive Compatibility (BIC) in fixed-design linear bandits (Sellke & Slivkins, 2023), we tackle the challenge of stochastic user covariates sampled online.