# Overcoming Prior Misspecification in Online Learning to Rank

arXiv: https://arxiv.org/abs/2301.10651 · HTML: https://mlsanity.com/paper/2301.10651/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 52 references that are in the map.
- First posted on arXiv 25 Jan 2023.

## Builds on (most cited first, 24 of 24)

- [Introducing LETOR 4.0 Datasets](https://mlsanity.com/paper/1306.2597/) (2013)
- [Improved Optimistic Algorithms for Logistic Bandits](https://mlsanity.com/paper/2002.07530/) (2020)
- [Cascading Bandits: Learning to Rank in the Cascade Model](https://mlsanity.com/paper/1502.02763/) (2015)
- [Feel-Good Thompson Sampling for Contextual Bandits and Reinforcement Learning](https://mlsanity.com/paper/2110.00871/) (2021)
- [Instance-Wise Minimax-Optimal Algorithms for Logistic Bandits](https://mlsanity.com/paper/2010.12642/) (2020)
- [Information-Theoretic Confidence Bounds for Reinforcement Learning](https://mlsanity.com/paper/1911.09724/) (2019)
- [Cascading Bandits for Large-Scale Recommendation Problems](https://mlsanity.com/paper/1603.05359/) (2016)
- [Jointly Efficient and Optimal Algorithms for Logistic Bandits](https://mlsanity.com/paper/2201.01985/) (2022)
- [On the Performance of Thompson Sampling on Logistic Bandits](https://mlsanity.com/paper/1905.04654/) (2019)
- [TopRank: A practical algorithm for online stochastic ranking](https://mlsanity.com/paper/1806.02248/) (2018)
- [Neural Models for Information Retrieval](https://mlsanity.com/paper/1705.01509/) (2017)
- [Multiple-Play Bandits in the Position-Based Model](https://mlsanity.com/paper/1606.02448/) (2016)
- [Online Learning to Rank in Stochastic Click Models](https://mlsanity.com/paper/1703.02527/) (2017)
- [Prior-free and prior-dependent regret bounds for Thompson Sampling](https://mlsanity.com/paper/1304.5758/) (2013)
- [PG-TS: Improved Thompson Sampling for Logistic Contextual Bandits](https://mlsanity.com/paper/1805.07458/) (2018)
- [Deep Hierarchy in Bandits](https://mlsanity.com/paper/2202.01454/) (2022)
- [Lifting the Information Ratio: An Information-Theoretic Analysis of Thompson Sampling for Contextual Bandits](https://mlsanity.com/paper/2205.13924/) (2022)
- [Thompson Sampling Algorithms for Cascading Bandits](https://mlsanity.com/paper/1810.01187/) (2018)
- [Maillard Sampling: Boltzmann Exploration Done Optimally](https://mlsanity.com/paper/2111.03290/) (2021)
- [Learning to Rank in the Position Based Model with Bandit Feedback](https://mlsanity.com/paper/2004.13106/) (2020)
- [Minimax Regret for Cascading Bandits](https://mlsanity.com/paper/2203.12577/) (2022)
- [Gaussian Imagination in Bandit Learning](https://mlsanity.com/paper/2201.01902/) (2022)
- [Multi-Task Learning for Email Search Ranking with Auxiliary Query Clustering](https://mlsanity.com/paper/1809.05618/) (2018)
- [Separate and Attend in Personal Email Search](https://mlsanity.com/paper/1911.09732/) (2019)

## Built on by (most cited first, 1 of 1)

- [Ranking with Popularity Bias: User Welfare under Self-Amplification Dynamics](https://mlsanity.com/paper/2305.18333/) (2023)

## 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.