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Overcoming Prior Misspecification in Online Learning to Rank

arXiv 2301.10651 ↗First posted 25 Jan 2023cs.LG cs.AIcited by 1

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Abstract (arXiv)

The recent literature on online learning to rank (LTR) has established the utility of prior knowledge to Bayesian ranking bandit algorithms. However, a major limitation of existing work is the requirement for the prior used by the algorithm to match the true prior. In this paper, we propose and analyze adaptive algorithms that address this issue and additionally extend these results to the linear and generalized linear models. We also consider scalar relevance feedback on top of click feedback. Moreover, we demonstrate the efficacy of our algorithms using both synthetic and real-world experiments.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 24 of its 52 references that are in the map. First posted on arXiv 25 Jan 2023.

Builds on

24 of 52 references.

References · 24

  1. 2013Introducing LETOR 4.0 Datasetscited by 120
  2. 2020Improved Optimistic Algorithms for Logistic Banditscited by 117
  3. 2015Cascading Bandits: Learning to Rank in the Cascade Modelcited by 75
  4. 2021Feel-Good Thompson Sampling for Contextual Bandits and Reinforcement Learningcited by 65
  5. 2020Instance-Wise Minimax-Optimal Algorithms for Logistic Banditscited by 56
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  9. 2019On the Performance of Thompson Sampling on Logistic Banditscited by 32
  10. 2018TopRank: A practical algorithm for online stochastic rankingcited by 30
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  12. 2016Multiple-Play Bandits in the Position-Based Modelcited by 27
12 more
  1. 2017Online Learning to Rank in Stochastic Click Modelscited by 26
  2. 2013Prior-free and prior-dependent regret bounds for Thompson Samplingcited by 25
  3. 2022Lifting the Information Ratio: An Information-Theoretic Analysis of Thompson Sampling for Contextual Banditscited by 19
  4. 2018PG-TS: Improved Thompson Sampling for Logistic Contextual Banditscited by 18
  5. 2022Deep Hierarchy in Banditscited by 18
  6. 2018Thompson Sampling Algorithms for Cascading Banditscited by 13
  7. 2021Maillard Sampling: Boltzmann Exploration Done Optimallycited by 13
  8. 2020Learning to Rank in the Position Based Model with Bandit Feedbackcited by 10
  9. 2022Minimax Regret for Cascading Banditscited by 10
  10. 2022Gaussian Imagination in Bandit Learningcited by 5
  11. 2018Multi-Task Learning for Email Search Ranking with Auxiliary Query Clusteringcited by 3
  12. 2019Separate and Attend in Personal Email Searchcited by 1

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Citing papers per year: 2023: 1.

1 paper.

  1. 2023Ranking with Popularity Bias: User Welfare under Self-Amplification Dynamicscited by 0