Preference-based learning for news headline recommendation
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
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| _version_ | 1866912418086518784 |
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| author | Bouras, Alexandre Durand, Audrey Khoury, Richard |
| author_facet | Bouras, Alexandre Durand, Audrey Khoury, Richard |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_06334 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Preference-based learning for news headline recommendation Bouras, Alexandre Durand, Audrey Khoury, Richard Information Retrieval Machine Learning 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. |
| title | Preference-based learning for news headline recommendation |
| topic | Information Retrieval Machine Learning |
| url | https://arxiv.org/abs/2506.06334 |