# Online Learning to Rank with Features

arXiv: https://arxiv.org/abs/1810.02567 · HTML: https://mlsanity.com/paper/1810.02567/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Oct 2018.

## Built on by (most cited first, 18 of 18)

- [Combinatorial Neural Bandits](https://mlsanity.com/paper/2306.00242/) (2023)
- [Sample Efficient Demonstration Selection for In-Context Learning](https://mlsanity.com/paper/2506.08607/) (2025)
- [Minimax Regret for Cascading Bandits](https://mlsanity.com/paper/2203.12577/) (2022)
- [PairRank: Online Pairwise Learning to Rank by Divide-and-Conquer](https://mlsanity.com/paper/2103.00368/) (2021)
- [Adversarial Attacks on Online Learning to Rank with Click Feedback](https://mlsanity.com/paper/2305.17071/) (2023)
- [Adversarial Attacks on Online Learning to Rank with Stochastic Click Models](https://mlsanity.com/paper/2305.19218/) (2023)
- [On Submodular Contextual Bandits](https://mlsanity.com/paper/2112.02165/) (2021)
- [Simultaneously Learning Stochastic and Adversarial Bandits under the Position-Based Model](https://mlsanity.com/paper/2207.05437/) (2022)
- [Calibrating Explore-Exploit Trade-off for Fair Online Learning to Rank](https://mlsanity.com/paper/2111.00735/) (2021)
- [Learning Neural Ranking Models Online from Implicit User Feedback](https://mlsanity.com/paper/2201.06658/) (2022)
- [Follow-the-Perturbed-Leader for Decoupled Bandits: Best-of-Both-Worlds and Practicality](https://mlsanity.com/paper/2510.12152/) (2025)
- [On Learning to Rank Long Sequences with Contextual Bandits](https://mlsanity.com/paper/2106.03546/) (2021)
- [UniRank: Unimodal Bandit Algorithm for Online Ranking](https://mlsanity.com/paper/2208.01515/) (2022)
- [Exploration of Unranked Items in Safe Online Learning to Re-Rank](https://mlsanity.com/paper/2305.01202/) (2023)
- [Adaptively Learning to Select-Rank in Online Platforms](https://mlsanity.com/paper/2406.05017/) (2024)
- [Online Learning to Rank under Corruption: A Robust Cascading Bandits Approach](https://mlsanity.com/paper/2511.03074/) (2025)
- [A Linear Matching Bandit Approach to Online Multi-Human Multi-Robot Teaming](https://mlsanity.com/paper/2606.29221/) (2026)
- [Data Efficient Sample Selection for In-Context Learning](https://mlsanity.com/paper/2609.06670/) (2026)

## Abstract (arXiv)

We introduce a new model for online ranking in which the click probability factors into an examination and attractiveness function and the attractiveness function is a linear function of a feature vector and an unknown parameter. Only relatively mild assumptions are made on the examination function. A novel algorithm for this setup is analysed, showing that the dependence on the number of items is replaced by a dependence on the dimension, allowing the new algorithm to handle a large number of items. When reduced to the orthogonal case, the regret of the algorithm improves on the state-of-the-art.