Preference-based learning for news headline recommendation

Alexandre Bouras, Audrey Durand, Richard Khoury
5/31/2025
cs.IRcs.LG

No confident code implementation found for this paper. 1 possible match can be shown below.

Abstract

This study explores strategies for optimizing news headline recommendations through preference-based learning. Using real-world data of user interactions with French-language online news posts, we learn a headline recommender agent under a contextual bandit setting. This allows us to explore the impact of translation on engagement predictions, as well as the benefits of different interactive strategies on user engagement during data collection. Our results show that explicit exploration may not be required in the presence of noisy contexts, opening the door to simpler but efficient strategies in practice.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. 1 weaker match is hidden by default — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Cite this paper

@article{bouras2025preferencebased,
  title  = {Preference-based learning for news headline recommendation},
  author = {Alexandre Bouras and Audrey Durand and Richard Khoury},
  year   = {2025},
  eprint = {2506.06334},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2506.06334v1}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2506.06334.svg)](https://codeofpaper.com/paper/2506.06334)

Discussion