In this study, we applied the ``personalized diversity nudge framework'' with the goal of expanding user reading coverage in terms of news locality (i.e., domestic and world news). We designed a novel topic-locality dual calibration algorithmic nudge and a large language model-based news personalization presentation nudge, then launched a 5-week real-user study with 120 U.S. news readers on the news recommendation experiment platform POPROX. With user interaction logs and survey responses, we found that algorithmic nudges can successfully increase exposure and consumption diversity, while the impact of LLM-based presentation nudges varied. User-level topic interest is a strong predictor of user clicks, while highlighting the relevance of news articles to prior read articles outperforms generic topic-based and no personalization. We also demonstrate that longitudinal exposure to calibrated news may shift readers' reading habits to value a balanced news digest from both domestic and world articles. Our results provide direction for future work on nudging for diverse consumption in news recommendation systems.

翻译：在本研究中，我们应用"个性化多样性助推框架"，旨在从新闻地域性（即国内新闻与世界新闻）维度扩展用户阅读覆盖范围。我们设计了一种新颖的主题-地域双重校准算法助推机制，以及一种基于大型语言模型的新闻个性化呈现助推机制，随后在新闻推荐实验平台POPROX上对120名美国新闻读者开展了为期5周的真实用户研究。通过用户交互日志与问卷调查反馈，我们发现算法助推能有效提升曝光与消费多样性，而基于LLM的呈现助推效果存在差异。用户层面的主题兴趣是预测点击行为的强指标，同时突出新闻文章与既往阅读内容相关性的策略优于通用主题推荐及无个性化方案。我们还证明，长期接触校准新闻可能促使读者转变阅读习惯，转而重视国内与国际报道的平衡性新闻摘要。本研究结果为新闻推荐系统中促进多样化消费的助推机制后续研究提供了方向。