# On Learning to Rank Long Sequences with Contextual Bandits

arXiv: https://arxiv.org/abs/2106.03546 · HTML: https://mlsanity.com/paper/2106.03546/

## 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.

## Builds on (most cited first, 19 of 19)

- [Online Learning under Delayed Feedback](https://mlsanity.com/paper/1306.0686/) (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)
- [Delay and Cooperation in Nonstochastic Bandits](https://mlsanity.com/paper/1602.04741/) (2016)
- [Relative Loss Bounds for On-line Density Estimation with the Exponential Family of Distributions](https://mlsanity.com/paper/1301.6677/) (2013)
- [Cascading Bandits for Large-Scale Recommendation Problems](https://mlsanity.com/paper/1603.05359/) (2016)
- [Improving Regret Bounds for Combinatorial Semi-Bandits with Probabilistically Triggered Arms and Its Applications](https://mlsanity.com/paper/1703.01610/) (2017)
- [Combinatorial Cascading Bandits](https://mlsanity.com/paper/1507.04208/) (2015)
- [Provably Optimal Algorithms for Generalized Linear Contextual Bandits](https://mlsanity.com/paper/1703.00048/) (2017)
- [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)
- [Online Stochastic Linear Optimization under One-bit Feedback](https://mlsanity.com/paper/1509.07728/) (2015)
- [Online Learning to Rank in Stochastic Click Models](https://mlsanity.com/paper/1703.02527/) (2017)
- [Online Clustering of Contextual Cascading Bandits](https://mlsanity.com/paper/1711.08594/) (2017)
- [Online Learning to Rank with Features](https://mlsanity.com/paper/1810.02567/) (2018)
- [Randomized Allocation with Nonparametric Estimation for Contextual Multi-Armed Bandits with Delayed Rewards](https://mlsanity.com/paper/1902.00819/) (2019)
- [Submodular Bandit Problem Under Multiple Constraints](https://mlsanity.com/paper/2006.00661/) (2020)
- [Cost-aware Cascading Bandits](https://mlsanity.com/paper/1805.08638/) (2018)
- [Cascading Non-Stationary Bandits: Online Learning to Rank in the Non-Stationary Cascade Model](https://mlsanity.com/paper/1905.12370/) (2019)

## 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.