# Learning from User Interactions with Rankings: A Unification of the Field

arXiv: https://arxiv.org/abs/2012.06576 · HTML: https://mlsanity.com/paper/2012.06576/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Dec 2020.

## Built on by (most cited first, 5 of 5)

- [Safe Deployment for Counterfactual Learning to Rank with Exposure-Based Risk Minimization](https://mlsanity.com/paper/2305.01522/) (2023)
- [Practical and Robust Safety Guarantees for Advanced Counterfactual Learning to Rank](https://mlsanity.com/paper/2407.19943/) (2024)
- [The Bandwagon Effect: Not Just Another Bias](https://mlsanity.com/paper/2206.12701/) (2022)
- [Recent Advances in the Foundations and Applications of Unbiased Learning to Rank](https://mlsanity.com/paper/2305.02914/) (2023)
- [Proximal Ranking Policy Optimization for Practical Safety in Counterfactual Learning to Rank](https://mlsanity.com/paper/2409.09881/) (2024)

## Abstract (arXiv)

Ranking systems form the basis for online search engines and recommendation services. They process large collections of items, for instance web pages or e-commerce products, and present the user with a small ordered selection. The goal of a ranking system is to help a user find the items they are looking for with the least amount of effort. Thus the rankings they produce should place the most relevant or preferred items at the top of the ranking. Learning to rank is a field within machine learning that covers methods which optimize ranking systems w.r.t. this goal. Traditional supervised learning to rank methods utilize expert-judgements to evaluate and learn, however, in many situations such judgements are impossible or infeasible to obtain. As a solution, methods have been introduced that perform learning to rank based on user clicks instead. The difficulty with clicks is that they are not only affected by user preferences, but also by what rankings were displayed. Therefore, these methods have to prevent being biased by other factors than user preference. This thesis concerns learning to rank methods based on user clicks and specifically aims to unify the different families of these methods. As a whole, the second part of this thesis proposes a framework that bridges many gaps between areas of online, counterfactual, and supervised learning to rank. It has taken approaches, previously considered independent, and unified them into a single methodology for widely applicable and effective learning to rank from user clicks.