Research map / Bandits and online learning / Contextual bandits and Thompson sampling
Online Learning to Rank in Stochastic Click Models
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use BatchRank instead of RankedExp3 | Online learning to rank on Yandex queries · CM and PBM | expected per-step regret | Helped |
| Use CascadeKL-UCB instead of BatchRank | Online learning to rank · CM | expected per-step regret | Helped |
| Use BatchRank instead of CascadeKL-UCB | Online learning to rank · PBM at T = 2M steps | expected per-step regret | Helped |
| Apply CascadeKL-UCB outside its intended cascade model | CascadeKL-UCB · PBM | expected per-step regret | Hurt |
| Use KL confidence intervals rather than UCB1 confidence intervals (ablation) | BatchRank · Stochastic click bandits | cumulative regret: O((α* - α)^-1) vs. O(χ^-1 (α* - α)^-1) | Helped |
| Remove item elimination from UpdateBatch (ablation) | BatchRank · Stochastic click bandits | regret dependence on number of items L: quadratic in L | Hurt |
Closest papers by meaning
- TopRank: A practical algorithm for online stochastic ranking (NeurIPS 2018 · similarity 0.91)
- DCM Bandits: Learning to Rank with Multiple Clicks (ICML 2016 · similarity 0.90)
- Online Learning to Rank with Features (ICML 2019 · similarity 0.88)
- Adaptively Learning to Select-Rank in Online Platforms (ICML 2024 · similarity 0.85)
- Identifiability Matters: Revealing the Hidden Recoverable Condition in Unbiased Learning to Rank (ICML 2024 · similarity 0.83)
- BayesCNS: A Unified Bayesian Approach to Address Cold Start and Non-Stationarity in Search Systems at Scale (AAAI 2025 · similarity 0.83)
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