Preference-based learning for news headline recommendation
This work addresses news recommendation for users, but it appears incremental as it builds on existing contextual bandit methods with specific data and insights.
This study tackled the problem of optimizing news headline recommendations using preference-based learning with real-world French-language news data, finding that explicit exploration may not be necessary in noisy contexts, which could lead to simpler and efficient strategies.
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.