# Incentivising Exploration and Recommendations for Contextual Bandits with Payments

arXiv: https://arxiv.org/abs/2001.07853 · HTML: https://mlsanity.com/paper/2001.07853/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jan 2020.

## Built on by (most cited first, 4 of 4)

- [Incentivizing Exploration in Linear Bandits under Information Gap](https://mlsanity.com/paper/2104.03860/) (2021)
- [Incentivizing Exploration with Linear Contexts and Combinatorial Actions](https://mlsanity.com/paper/2306.01990/) (2023)
- [Incentivized Bandit Learning with Self-Reinforcing User Preferences](https://mlsanity.com/paper/2105.08869/) (2021)
- [Geometry Meets Incentives: Sample-Efficient Incentivized Exploration with Linear Contexts](https://mlsanity.com/paper/2506.01685/) (2025)

## Abstract (arXiv)

We propose a contextual bandit based model to capture the learning and social welfare goals of a web platform in the presence of myopic users. By using payments to incentivize these agents to explore different items/recommendations, we show how the platform can learn the inherent attributes of items and achieve a sublinear regret while maximizing cumulative social welfare. We also calculate theoretical bounds on the cumulative costs of incentivization to the platform. Unlike previous works in this domain, we consider contexts to be completely adversarial, and the behavior of the adversary is unknown to the platform. Our approach can improve various engagement metrics of users on e-commerce stores, recommendation engines and matching platforms.