# Learning with Exposure Constraints in Recommendation Systems

arXiv: https://arxiv.org/abs/2302.01377 · HTML: https://mlsanity.com/paper/2302.01377/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 49 references that are in the map.
- First posted on arXiv 2 Feb 2023.

## Builds on (most cited first, 30 of 30)

- [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019)
- [Introduction to Multi-Armed Bandits](https://mlsanity.com/paper/1904.07272/) (2019)
- [Strategic Classification](https://mlsanity.com/paper/1506.06980/) (2015)
- [Fairness of Exposure in Rankings](https://mlsanity.com/paper/1802.07281/) (2018)
- [Fairness in Recommendation Ranking through Pairwise Comparisons](https://mlsanity.com/paper/1903.00780/) (2019)
- [A Survey on the Fairness of Recommender Systems](https://mlsanity.com/paper/2206.03761/) (2022)
- [Achieving Fairness in the Stochastic Multi-armed Bandit Problem](https://mlsanity.com/paper/1907.10516/) (2019)
- [Multisided Fairness for Recommendation](https://mlsanity.com/paper/1707.00093/) (2017)
- [Rotting Bandits](https://mlsanity.com/paper/1702.07274/) (2017)
- [Fair Contextual Multi-Armed Bandits: Theory and Experiments](https://mlsanity.com/paper/1912.08055/) (2019)
- [Fair Algorithms for Multi-Agent Multi-Armed Bandits](https://mlsanity.com/paper/2007.06699/) (2020)
- [Modeling Content Creator Incentives on Algorithm-Curated Platforms](https://mlsanity.com/paper/2206.13102/) (2022)
- [A Game-Theoretic Approach to Recommendation Systems with Strategic Content Providers](https://mlsanity.com/paper/1806.00955/) (2018)
- [Optimizing Long-term Social Welfare in Recommender Systems: A Constrained Matching Approach](https://mlsanity.com/paper/2008.00104/) (2020)
- [Recovering Bandits](https://mlsanity.com/paper/1910.14354/) (2019)
- [The Price of Incentivizing Exploration: A Characterization via Thompson Sampling and Sample Complexity](https://mlsanity.com/paper/2002.00558/) (2020)
- [Exploration-Exploitation in Multi-Agent Competition: Convergence with Bounded Rationality](https://mlsanity.com/paper/2106.12928/) (2021)
- [Rebounding Bandits for Modeling Satiation Effects](https://mlsanity.com/paper/2011.06741/) (2020)
- [A Framework for Adapting Offline Algorithms to Solve Combinatorial Multi-Armed Bandit Problems with Bandit Feedback](https://mlsanity.com/paper/2301.13326/) (2023)
- [Competition, Alignment, and Equilibria in Digital Marketplaces](https://mlsanity.com/paper/2208.14423/) (2022)
- [Multi-armed Bandit Problems with Strategic Arms](https://mlsanity.com/paper/1706.09060/) (2017)
- [The Intrinsic Robustness of Stochastic Bandits to Strategic Manipulation](https://mlsanity.com/paper/1906.01528/) (2019)
- [Modeling Attrition in Recommender Systems with Departing Bandits](https://mlsanity.com/paper/2203.13423/) (2022)
- [Towards Content Provider Aware Recommender Systems: A Simulation Study on the Interplay between User and Provider Utilities](https://mlsanity.com/paper/2105.02377/) (2021)
- [Towards Fair Recommendation in Two-Sided Platforms](https://mlsanity.com/paper/2201.01180/) (2021)
- [Linear Regression from Strategic Data Sources](https://mlsanity.com/paper/1309.7824/) (2013)
- [Efficient Algorithms for Planning with Participation Constraints](https://mlsanity.com/paper/2205.07767/) (2022)
- [Trust and Trustworthiness in Social Recommender Systems](https://mlsanity.com/paper/1903.01780/) (2019)
- [Exploration, Exploitation, and Engagement in Multi-Armed Bandits with Abandonment](https://mlsanity.com/paper/2205.13566/) (2022)
- [Dynamic Learning of Sequential Choice Bandit Problem under Marketing Fatigue](https://mlsanity.com/paper/1903.08193/) (2019)

## Built on by (most cited first, 8 of 8)

- [Clickbait vs. Quality: How Engagement-Based Optimization Shapes the Content Landscape in Online Platforms](https://mlsanity.com/paper/2401.09804/) (2024)
- [Accounting for AI and Users Shaping One Another: The Role of Mathematical Models](https://mlsanity.com/paper/2404.12366/) (2024)
- [Incentivizing High-Quality Content in Online Recommender Systems](https://mlsanity.com/paper/2306.07479/) (2023)
- [Flattening Supply Chains: When do Technology Improvements lead to Disintermediation?](https://mlsanity.com/paper/2502.20783/) (2025)
- [Incentivized Collaboration in Active Learning](https://mlsanity.com/paper/2311.00260/) (2023)
- [Countering Mainstream Bias via End-to-End Adaptive Local Learning](https://mlsanity.com/paper/2404.08887/) (2024)
- [Bandits with Single-Peaked Preferences and Limited Resources](https://mlsanity.com/paper/2510.09425/) (2025)
- [On Incentivized Exploration beyond Bayesianism and Full-Information](https://mlsanity.com/paper/2607.18300/) (2026)

## Abstract (arXiv)

Recommendation systems are dynamic economic systems that balance the needs of multiple stakeholders. A recent line of work studies incentives from the content providers' point of view. Content providers, e.g., vloggers and bloggers, contribute fresh content and rely on user engagement to create revenue and finance their operations. In this work, we propose a contextual multi-armed bandit setting to model the dependency of content providers on exposure. In our model, the system receives a user context in every round and has to select one of the arms. Every arm is a content provider who must receive a minimum number of pulls every fixed time period (e.g., a month) to remain viable in later rounds; otherwise, the arm departs and is no longer available. The system aims to maximize the users' (content consumers) welfare. To that end, it should learn which arms are vital and ensure they remain viable by subsidizing arm pulls if needed. We develop algorithms with sub-linear regret, as well as a lower bound that demonstrates that our algorithms are optimal up to logarithmic factors.