mlsanityData to 14 Oct 2026

Multiple-Play Bandits in the Position-Based Model

arXiv 1606.02448 ↗First posted 8 Jun 2016cs.LG math.ST stat.THcited by 28

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Abstract (arXiv)

Sequentially learning to place items in multi-position displays or lists is a task that can be cast into the multiple-play semi-bandit setting. However, a major concern in this context is when the system cannot decide whether the user feedback for each item is actually exploitable. Indeed, much of the content may have been simply ignored by the user. The present work proposes to exploit available information regarding the display position bias under the so-called Position-based click model (PBM). We first discuss how this model differs from the Cascade model and its variants considered in several recent works on multiple-play bandits. We then provide a novel regret lower bound for this model as well as computationally efficient algorithms that display good empirical and theoretical performance.

Key facts. Cited by 28 papers in the map (data through 14 Oct 2026). First posted on arXiv 8 Jun 2016.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 1; 2022: 7; 2023: 5; 2024: 6; 2025: 6; 2026: 3.

28 papers.

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  1. 2022Doubly-Robust Estimation for Correcting Position-Bias in Click Feedback for Unbiased Learning to Rankcited by 18
  2. 2026A Survey on LLM-based Conversational User Simulationcited by 18
  3. 2024Optimal Design for Human Preference Elicitationcited by 17
  4. 2024Multi-Armed Bandits with Interferencecited by 10
  5. 2025Active Learning for Direct Preference Optimizationcited by 10
  6. 2024Online Experimental Design With Estimation-Regret Trade-off Under Network Interferencecited by 9
  7. 2021Robust Generalization and Safe Query-Specialization in Counterfactual Learning to Rankcited by 6
  8. 2022Multiple-Play Stochastic Bandits with Shareable Finite-Capacity Armscited by 6
  9. 2023Adversarial Attacks on Online Learning to Rank with Click Feedbackcited by 6
  10. 2023Adversarial Attacks on Online Learning to Rank with Stochastic Click Modelscited by 6
  11. 2025Graph-Dependent Regret Bounds in Multi-Armed Bandits with Interferencecited by 5
  12. 2022Probabilistic Permutation Graph Search: Black-Box Optimization for Fairness in Rankingcited by 4
16 more
  1. 2024Cascading Reinforcement Learningcited by 4
  2. 2022Simultaneously Learning Stochastic and Adversarial Bandits under the Position-Based Modelcited by 2
  3. 2022Contextual bandits with concave rewards, and an application to fair rankingcited by 2
  4. 2023Bandit Learning to Rank with Position-Based Click Models: Personalized and Equal Treatmentscited by 2
  5. 2022Ranking In Generalized Linear Banditscited by 1
  6. 2023Overcoming Prior Misspecification in Online Learning to Rankcited by 1
  7. 2024Misalignment, Learning, and Ranking: Harnessing Users Limited Attentioncited by 1
  8. 2025Design-Based Bandits Under Network Interference: Trade-Off Between Regret and Statistical Inferencecited by 1
  9. 2022UniRank: Unimodal Bandit Algorithm for Online Rankingcited by 0
  10. 2023Exploration of Unranked Items in Safe Online Learning to Re-Rankcited by 0
  11. 2024Adaptively Learning to Select-Rank in Online Platformscited by 0
  12. 2025Observation-Free Attacks on Online Learning to Rankcited by 0
  13. 2025Online Learning to Rank under Corruption: A Robust Cascading Bandits Approachcited by 0
  14. 2025Multiple-play Stochastic Bandits with Prioritized Arm Capacity Sharingcited by 0
  15. 2026Contextual Slate GLM Bandits with Limited Adaptivitycited by 0
  16. 2026Ranking Bandits for Carousel Interfaces with Observable Browsing Depthcited by 0