用双校准与LLM提示提升新闻阅读的本地与全球平衡
Balancing Domestic and Global Perspectives: Evaluating Dual-Calibration and LLM-Generated Nudges for Diverse News Recommendation
- 设计双校准算法与LLM生成提示，引导用户接触更多元新闻
- 实验显示算法提示显著提升本地与国际新闻的阅读多样性
- 长期使用可改变读者习惯，更偏好国内外均衡的新闻摘要
本研究采用个性化多样性提示框架，旨在扩大用户在新闻地域覆盖（即国内与国际新闻）上的阅读范围。我们设计了一种新的主题-地域双校准算法提示和基于大语言模型的新闻个性化展示提示，并在真实用户平台上开展为期5周的实验，参与用户为120名美国新闻读者。通过用户交互日志与问卷反馈发现，算法提示能有效提升新闻曝光与消费多样性；而基于LLM的展示提示效果不一。用户层面的主题兴趣是点击的重要预测因素，突出文章与先前阅读内容的相关性优于通用主题或无个性化推荐。此外，长期接触经过校准的新闻后，读者的阅读习惯会逐渐转向更重视国内外新闻均衡的资讯汇编。研究结果为新闻推荐系统中促进多样化消费提供了新方向。
原文摘要 · Abstract (English)
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.
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