# R-UCB: a Contextual Bandit Algorithm for Risk-Aware Recommender Systems

arXiv: https://arxiv.org/abs/1408.2195 · HTML: https://mlsanity.com/paper/1408.2195/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Aug 2014.

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

- [Etat de l'art sur l'application des bandits multi-bras](https://mlsanity.com/paper/2101.00001/) (2021)

## Abstract (arXiv)

Mobile Context-Aware Recommender Systems can be naturally modelled as an exploration/exploitation trade-off (exr/exp) problem, where the system has to choose between maximizing its expected rewards dealing with its current knowledge (exploitation) and learning more about the unknown user's preferences to improve its knowledge (exploration). This problem has been addressed by the reinforcement learning community but they do not consider the risk level of the current user's situation, where it may be dangerous to recommend items the user may not desire in her current situation if the risk level is high. We introduce in this paper an algorithm named R-UCB that considers the risk level of the user's situation to adaptively balance between exr and exp. The detailed analysis of the experimental results reveals several important discoveries in the exr/exp behaviour.