Incentivized Exploration with Stochastic Covariates: A Two-Stage Mechanism Design for Recommender System
arXiv:2406.04374v2 Announce Type: replace-cross Abstract: 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. Unlike standard black-box reductions (M
The proliferation of AI-driven platforms necessitates more sophisticated incentive mechanisms to balance exploration and exploitation, especially as user behavior becomes increasingly complex and data-rich.
This research addresses fundamental challenges in designing fair and effective recommender systems, which are critical infrastructure for the internet economy and profoundly influence user experience and market dynamics.
The proposed two-stage mechanism design offers a new approach to incentivizing exploration in recommender systems, potentially leading to more diverse recommendations, fairer outcomes for creators, and improved robustness against strategic user behavior.
- · AI developers
- · E-commerce platforms
- · Content creators
- · Users
- · Platforms with naive recommendation algorithms
- · Content lacking exploration incentives
Improved recommender system performance and user satisfaction on various platforms due to better incentive alignment.
Increased ability for platforms to discover and promote new products or content, fostering innovation and competition across industries.
Potential for new ethical and regulatory discussions around 'algorithmic nudging' and the design of self-interested yet socially beneficial AI systems.
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Read at arXiv cs.LG