用用户偏好优化新闻标题推荐，提升点击率。
Preference-based learning for news headline recommendation
- 基于上下文老虎机框架，学习用户偏好来推荐标题。
- 在法语新闻数据上验证，噪声环境下无需刻意探索。
- 适合做个性化推荐系统的研究与实践者。
本研究探讨了通过基于偏好的学习优化新闻标题推荐的策略。利用真实世界中用户对法语在线新闻内容的交互数据，在上下文老虎机设置下训练标题推荐智能体。该方法可分析翻译对用户参与度预测的影响，以及不同交互策略在数据收集阶段对用户参与度的提升效果。结果表明，在存在噪声上下文的情况下，显式探索并非必要，为实际应用中简化但高效的策略提供了可能。
原文摘要 · Abstract (English)
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.
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