Research map / Bandits and online learning / Contextual bandits and Thompson sampling
Ranking bandits
Learn to rank online from click feedback with unimodal and semi-bandit methods.
42 accepted papers at ICML, NeurIPS, ICLR, CVPR and AAAI (2016–2026) · an approach in Contextual bandits and Thompson sampling, keyword cluster “contextual bandits · contexts · user”
State History Tried Preprints Who Export
Where it stands
Not enough benchmark data Fewer than four papers report the same benchmark, so there is no record to follow.
7 papers in 2023–24 and 12 in 2025–26 (at ICML, NeurIPS, ICLR, CVPR and AAAI); its share of the topic grew, from 4.7% to 9.0%.
How this is decided
A benchmark is tracked when at least four papers of this approach report their own method on the same dataset and metric in at least two different years. Its record is the best number reported up to 2024 compared with the best reported in 2025–26: still improving means at least 1% better, flat means at most 0.2% better with at least two papers from 2025–26 trying. Numbers on a different scale from the rest (a fraction among percentages) and far outliers are left out. Results come from different papers with different setups, so they are signals, not head-to-head comparisons. The share of papers is counted at ICML, NeurIPS, ICLR, CVPR and AAAI.
How it got here
Most cited paper of each year
- 2016 · DCM Bandits: Learning to Rank with Multiple Clicks (ICML 2016 · 55 citations)
- 2017 · Factorization Bandits for Interactive Recommendation (AAAI 2017 · 97 citations)
- 2018 · TopRank: A practical algorithm for online stochastic ranking (NeurIPS 2018 · 33 citations)
- 2019 · Online Learning to Rank with Features (ICML 2019 · 10 citations)
- 2020 · Diversified Interactive Recommendation with Implicit Feedback (AAAI 2020 · 71 citations)
- 2021 · A Hybrid Bandit Framework for Diversified Recommendation (AAAI 2021 · 23 citations)
- 2022 · Achieving Counterfactual Fairness for Causal Bandit (AAAI 2022 · 18 citations)
- 2023 · Online Low Rank Matrix Completion (ICLR 2023 · 2 citations)
- 2024 · Online Matrix Completion: A Collaborative Approach with Hott Items (ICML 2024 · 1 citation)
- 2025 · A Parametric Contextual Online Learning Theory of Brokerage (ICML 2025 · 0 citations)
- 2026 · Keep Everyone Happy: Online Fair Division of Numerous Items with Few Copies (ICML 2026 · 0 citations)
Earliest papers here
- DCM Bandits: Learning to Rank with Multiple Clicks (ICML 2016 · 55 citations)
- Bounded Off-Policy Evaluation with Missing Data for Course Recommendation and Curriculum Design (ICML 2016 · 16 citations)
- Barron and Cover’s Theory in Supervised Learning and its Application to Lasso (ICML 2016 · 3 citations)
What has been tried
33 papers here report 210 measured changes (a component added, removed or swapped, compared with a baseline): helped 66%, no clear effect 10%, hurt 20%, mixed 5%. Papers mostly publish changes that helped, so "helped" is over-represented.
Recent preprints
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Who publishes on it
Institutions on the most papers of this approach since 2023 (of 19 papers; from OpenAlex and the papers' affiliations).
- Adobe · 2 papers
- University of Wisconsin - Madison · 2 papers
Use it
Download BibTeX of 34 key papersOpen the topic in the interactive map
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Related approaches
Same keyword cluster
- Oracle-efficient contextual bandits (168 papers)
- Linear and generalized linear bandits (88 papers)
- Practical contextual bandit extensions (77 papers)
- Causal and structured bandits (61 papers)
Elsewhere in the topic
- Thompson sampling analysis (84 papers)
- Gaussian process bandit regret (26 papers)
Benchmark numbers, ablations and baseline numbers are extracted by AI from each paper's full text and can be wrong; the paper is the authority. Data: results index of 2026-11-02, citation counts from OpenAlex (2026-10-09).