Personalized news recommendations have become a standard feature of large news aggregation services, optimizing user engagement through automated content selection. In contrast, legacy news media often approach personalization cautiously, striving to balance technological innovation with core editorial values. As a result, online platforms of traditional news outlets typically combine editorially curated content with algorithmically selected articles - a strategy we term controlled personalization. In this industry article, we evaluate the effectiveness of controlled personalization through an A/B test conducted on the website of a major Norwegian legacy news organization. Our findings indicate that even a modest level of personalization yields substantial benefits. Specifically, we observe that users exposed to personalized content demonstrate higher click-through-rates and reduced navigation effort, suggesting improved discovery of relevant content. Moreover, our analysis reveals that controlled personalization contributes to greater content diversity and catalog coverage and in addition reduces popularity bias. Overall, our results suggest that controlled personalization can successfully align user needs with editorial goals, offering a viable path for legacy media to adopt personalization technologies while upholding journalistic values.
Do We Care About Personalization and Explainability? An Interview Study with News Recommendation Engineers
University of Amsterdam（阿姆斯特丹大学）；Jheronimus Academy of Data Science（耶罗尼穆斯数据科学学院）；Birla Institute of Technology & Science, Pilani（比拉理工学院皮拉尼校区）；Eindhoven University of Technology（埃因霍温理工大学）
Algorithms and System Architecture for Immediate Personalized News Recommendations
个性化在许多服务中扮演着重要的角色，就像新闻一样。许多研究已经检查了新闻个性化算法，但很少考虑实际环境。本文提出了在实际环境中生成即时个性化新闻的算法和系统架构。即时性意味着新闻趋势和用户兴趣的变化会迅速反映在推荐新闻列表中。由于新闻趋势和用户兴趣迅速变化，即时性在新闻个性化应用中至关重要。我们开发算法和系统架构来实现即时性。我们的算法是基于用户集群的协同过滤，并评估新闻文章使用点击率和衰减分数的基础上经过的时间，因为用户的最后一次访问。现有的研究还没有充分讨论系统架构，所以本文的一个主要贡献是，我们展示了一个系统架构，实现我们的算法和配置的例子上实现的Amazon Web Services。我们评估所提出的方法离线和在线。离线实验通过来自商业新闻交付服务的真实世界数据集进行，在线实验通过生产环境上的A/B测试进行。我们证实了我们提出的方法的有效性，我们的系统架构可以在大规模的生产环境中运行。
LLM-Assisted Reranking to Operationalize Nuanced Objectives in Recommender Systems
Department of Communication, University of California, Los Angeles, CA 90095（通信系，加州大学洛杉矶分校，CA 90095）；Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA 19104（计算机与信息科学系，宾夕法尼亚大学，Philadelphia, PA 19104）；Amenenberg School of Communication, University of Pennsylvania, Philadelphia, PA 19104（安纳伯格通信学院，宾夕法尼亚大学，Philadelphia, PA 19104）；Operations, Information, and Decisions Department, University of Pennsylvania, Philadelphia, PA 19104（运营、信息与决策系，宾夕法尼亚大学，Philadelphia, PA 19104）
