# Ranking In Generalized Linear Bandits

arXiv: https://arxiv.org/abs/2207.00109 · HTML: https://mlsanity.com/paper/2207.00109/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 29 references that are in the map.
- First posted on arXiv 30 Jun 2022.

## Builds on (most cited first, 7 of 7)

- [A Tutorial on Thompson Sampling](https://mlsanity.com/paper/1707.02038/) (2017)
- [Multi-Armed Bandits with Correlated Arms](https://mlsanity.com/paper/1911.03959/) (2019)
- [An Efficient Algorithm For Generalized Linear Bandit: Online Stochastic Gradient Descent and Thompson Sampling](https://mlsanity.com/paper/2006.04012/) (2020)
- [Multiple-Play Bandits in the Position-Based Model](https://mlsanity.com/paper/1606.02448/) (2016)
- [Feedback graph regret bounds for Thompson Sampling and UCB](https://mlsanity.com/paper/1905.09898/) (2019)
- [On the Prior Sensitivity of Thompson Sampling](https://mlsanity.com/paper/1506.03378/) (2015)
- [Contextual Bandits with Side-Observations](https://mlsanity.com/paper/2006.03951/) (2020)

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

- [Adaptively Learning to Select-Rank in Online Platforms](https://mlsanity.com/paper/2406.05017/) (2024)

## Abstract (arXiv)

We study the ranking problem in generalized linear bandits. At each time, the learning agent selects an ordered list of items and observes stochastic outcomes. In recommendation systems, displaying an ordered list of the most attractive items is not always optimal as both position and item dependencies result in a complex reward function. A very naive example is the lack of diversity when all the most attractive items are from the same category. We model the position and item dependencies in the ordered list and design UCB and Thompson Sampling type algorithms for this problem. Our work generalizes existing studies in several directions, including position dependencies where position discount is a particular case, and connecting the ranking problem to graph theory.