Preference-based learning for news headline recommendation
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 13 Oct 2026). Builds on 8 of its 21 references that are in the map. First posted on arXiv 31 May 2025.
Builds on
8 of 21 references.
References · 8
- 2022Training language models to follow instructions with human feedbackcited by 15,856
- 2019Optuna: A Next-generation Hyperparameter Optimization Frameworkcited by 2,472
- 2024NV-Embed: Improved Techniques for Training LLMs as Generalist Embedding Modelscited by 467
- 2020Neural Thompson Samplingcited by 128
- 2024Making Text Embedders Few-Shot Learnerscited by 79
- 2020The Unreasonable Effectiveness of Greedy Algorithms in Multi-Armed Bandit with Many Armscited by 27
- 2019Pairwise Learning to Rank by Neural Networks Revisited: Reconstruction, Theoretical Analysis and Practical Performancecited by 13
- 2014Bandits Warm-up Cold Recommender Systemscited by 4
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