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On Learning to Rank Long Sequences with Contextual Bandits

arXiv 2106.03546 ↗First posted 7 Jun 2021cs.LG cs.AIcited by 0

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Abstract (arXiv)

Motivated by problems of learning to rank long item sequences, we introduce a variant of the cascading bandit model that considers flexible length sequences with varying rewards and losses. We formulate two generative models for this problem within the generalized linear setting, and design and analyze upper confidence algorithms for it. Our analysis delivers tight regret bounds which, when specialized to vanilla cascading bandits, results in sharper guarantees than previously available in the literature. We evaluate our algorithms on a number of real-world datasets, and show significantly improved empirical performance as compared to known cascading bandit baselines.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 19 of its 40 references that are in the map. First posted on arXiv 7 Jun 2021.

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19 of 40 references.

References · 19

  1. 2013Online Learning under Delayed Feedbackcited by 141
  2. 2020Improved Optimistic Algorithms for Logistic Banditscited by 118
  3. 2015Cascading Bandits: Learning to Rank in the Cascade Modelcited by 77
  4. 2016Delay and Cooperation in Nonstochastic Banditscited by 71
  5. 2013Relative Loss Bounds for On-line Density Estimation with the Exponential Family of Distributionscited by 60
  6. 2016Cascading Bandits for Large-Scale Recommendation Problemscited by 45
  7. 2017Improving Regret Bounds for Combinatorial Semi-Bandits with Probabilistically Triggered Arms and Its Applicationscited by 45
  8. 2015Combinatorial Cascading Banditscited by 40
  9. 2017Provably Optimal Algorithms for Generalized Linear Contextual Banditscited by 38
  10. 2019On the Performance of Thompson Sampling on Logistic Banditscited by 32
  11. 2018TopRank: A practical algorithm for online stochastic rankingcited by 30
  12. 2015Online Stochastic Linear Optimization under One-bit Feedbackcited by 26
7 more
  1. 2017Online Learning to Rank in Stochastic Click Modelscited by 26
  2. 2017Online Clustering of Contextual Cascading Banditscited by 24
  3. 2018Online Learning to Rank with Featurescited by 18
  4. 2019Randomized Allocation with Nonparametric Estimation for Contextual Multi-Armed Bandits with Delayed Rewardscited by 15
  5. 2020Submodular Bandit Problem Under Multiple Constraintscited by 15
  6. 2018Cost-aware Cascading Banditscited by 6
  7. 2019Cascading Non-Stationary Bandits: Online Learning to Rank in the Non-Stationary Cascade Modelcited by 5

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