# Bandits Meet Mechanism Design to Combat Clickbait in Online Recommendation

arXiv: https://arxiv.org/abs/2311.15647 · HTML: https://mlsanity.com/paper/2311.15647/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 39 references that are in the map.
- First posted on arXiv 27 Nov 2023.

## Builds on (most cited first, 14 of 14)

- [Introduction to Multi-Armed Bandits](https://mlsanity.com/paper/1904.07272/) (2019)
- [Strategic Classification](https://mlsanity.com/paper/1506.06980/) (2015)
- [Explore First, Exploit Next: The True Shape of Regret in Bandit Problems](https://mlsanity.com/paper/1602.07182/) (2016)
- [Clicks can be Cheating: Counterfactual Recommendation for Mitigating Clickbait Issue](https://mlsanity.com/paper/2009.09945/) (2020)
- [Cascading Bandits for Large-Scale Recommendation Problems](https://mlsanity.com/paper/1603.05359/) (2016)
- [Modeling Content Creator Incentives on Algorithm-Curated Platforms](https://mlsanity.com/paper/2206.13102/) (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)
- [VCG Mechanism Design with Unknown Agent Values under Stochastic Bandit Feedback](https://mlsanity.com/paper/2004.08924/) (2020)
- [Combinatorial Bandits under Strategic Manipulations](https://mlsanity.com/paper/2102.12722/) (2021)
- [Incentivizing High-Quality Content in Online Recommender Systems](https://mlsanity.com/paper/2306.07479/) (2023)
- [Multi-armed Bandit Algorithm against Strategic Replication](https://mlsanity.com/paper/2110.12160/) (2021)
- [Online Learning in Contextual Second-Price Pay-Per-Click Auctions](https://mlsanity.com/paper/2310.05047/) (2023)
- [On the Robustness of Epoch-Greedy in Multi-Agent Contextual Bandit Mechanisms](https://mlsanity.com/paper/2307.07675/) (2023)

## Built on by (most cited first, 6 of 6)

- [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)
- [Can Probabilistic Feedback Drive User Impacts in Online Platforms?](https://mlsanity.com/paper/2401.05304/) (2024)
- [Strategic Linear Contextual Bandits](https://mlsanity.com/paper/2406.00551/) (2024)
- [Strategic Multi-Armed Bandit Problems Under Debt-Free Reporting](https://mlsanity.com/paper/2501.16018/) (2025)
- [COBRA: Contextual Bandit Algorithm for Ensuring Truthful Strategic Agents](https://mlsanity.com/paper/2505.23720/) (2025)

## Abstract (arXiv)

We study a strategic variant of the multi-armed bandit problem, which we coin the strategic click-bandit. This model is motivated by applications in online recommendation where the choice of recommended items depends on both the click-through rates and the post-click rewards. Like in classical bandits, rewards follow a fixed unknown distribution. However, we assume that the click-rate of each arm is chosen strategically by the arm (e.g., a host on Airbnb) in order to maximize the number of times it gets clicked. The algorithm designer does not know the post-click rewards nor the arms' actions (i.e., strategically chosen click-rates) in advance, and must learn both values over time. To solve this problem, we design an incentive-aware learning algorithm, UCB-S, which achieves two goals simultaneously: (a) incentivizing desirable arm behavior under uncertainty; (b) minimizing regret by learning unknown parameters. We characterize all approximate Nash equilibria among arms under UCB-S and show a tildeO (√KT) regret bound uniformly in every equilibrium. We also show that incentive-unaware algorithms generally fail to achieve low regret in the strategic click-bandit. Finally, we support our theoretical results by simulations of strategic arm behavior which confirm the effectiveness and robustness of our proposed incentive design.