mlsanityData to 13 Oct 2026

Online Learning to Rank with Features

arXiv 1810.02567 ↗First posted 5 Oct 2018stat.ML cs.LGcited by 18

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Abstract (arXiv)

We introduce a new model for online ranking in which the click probability factors into an examination and attractiveness function and the attractiveness function is a linear function of a feature vector and an unknown parameter. Only relatively mild assumptions are made on the examination function. A novel algorithm for this setup is analysed, showing that the dependence on the number of items is replaced by a dependence on the dimension, allowing the new algorithm to handle a large number of items. When reduced to the orthogonal case, the regret of the algorithm improves on the state-of-the-art.

Key facts. Cited by 18 papers in the map (data through 13 Oct 2026). First posted on arXiv 5 Oct 2018.

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Cited by year (2026 to 13 Oct)

Citing papers per year: 2021: 4; 2022: 4; 2023: 4; 2024: 1; 2025: 3; 2026: 2.

18 papers.

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  1. 2023Combinatorial Neural Banditscited by 16
  2. 2025Sample Efficient Demonstration Selection for In-Context Learningcited by 12
  3. 2022Minimax Regret for Cascading Banditscited by 10
  4. 2021PairRank: Online Pairwise Learning to Rank by Divide-and-Conquercited by 6
  5. 2023Adversarial Attacks on Online Learning to Rank with Click Feedbackcited by 6
  6. 2023Adversarial Attacks on Online Learning to Rank with Stochastic Click Modelscited by 6
  7. 2021On Submodular Contextual Banditscited by 2
  8. 2022Simultaneously Learning Stochastic and Adversarial Bandits under the Position-Based Modelcited by 2
  9. 2021Calibrating Explore-Exploit Trade-off for Fair Online Learning to Rankcited by 1
  10. 2022Learning Neural Ranking Models Online from Implicit User Feedbackcited by 1
  11. 2025Follow-the-Perturbed-Leader for Decoupled Bandits: Best-of-Both-Worlds and Practicalitycited by 1
  12. 2021On Learning to Rank Long Sequences with Contextual Banditscited by 0
6 more
  1. 2022UniRank: Unimodal Bandit Algorithm for Online Rankingcited by 0
  2. 2023Exploration of Unranked Items in Safe Online Learning to Re-Rankcited by 0
  3. 2024Adaptively Learning to Select-Rank in Online Platformscited by 0
  4. 2025Online Learning to Rank under Corruption: A Robust Cascading Bandits Approachcited by 0
  5. 2026A Linear Matching Bandit Approach to Online Multi-Human Multi-Robot Teamingcited by 0
  6. 2026Data Efficient Sample Selection for In-Context Learningcited by 0