# Freshness-Aware Thompson Sampling

arXiv: https://arxiv.org/abs/1409.8572 · HTML: https://mlsanity.com/paper/1409.8572/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Sep 2014.

## Built on by (most cited first, 2 of 2)

- [Survey on Applications of Neurosymbolic Artificial Intelligence](https://mlsanity.com/paper/2209.12618/) (2022)
- [Etat de l'art sur l'application des bandits multi-bras](https://mlsanity.com/paper/2101.00001/) (2021)

## Abstract (arXiv)

To follow the dynamicity of the user's content, researchers have recently started to model interactions between users and the Context-Aware Recommender Systems (CARS) as a bandit problem where the system needs to deal with exploration and exploitation dilemma. In this sense, we propose to study the freshness of the user's content in CARS through the bandit problem. We introduce in this paper an algorithm named Freshness-Aware Thompson Sampling (FA-TS) that manages the recommendation of fresh document according to the user's risk of the situation. The intensive evaluation and the detailed analysis of the experimental results reveals several important discoveries in the exploration/exploitation (exr/exp) behaviour.