我们关心个性化和可解释性吗？一项面向新闻推荐工程师的访谈研究
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）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究通过访谈九家新闻机构的工程师，发现个性化因用户追踪、编辑控制等顾虑而受限，可解释性常被忽视，并提供了在新闻推荐流程中采纳可解释性的实用指南。
AI中文摘要：
关于推荐系统可解释性的研究主要聚焦于终端用户，而忽视了那些构建和维护这些系统的人员及其潜在用例（如模型调试）的视角。在本研究中，我们考察了新闻工程师及相关技术利益相关者如何在实践中感知和实施个性化与可解释性。我们在九家新闻机构中进行了15次半结构化访谈，覆盖公共和私营部门的不同地区，以探究影响其方法选择的挑战和动机。我们的研究结果显示，对新闻机构而言，个性化并非总是一个直接或理想的选择，因为围绕用户追踪、编辑控制和资源限制的担忧常常限制其采用。即使在已投产的个性化新闻推荐系统中，可解释性也很少被优先考虑，日常运营需求往往优先于长期透明度目标。各机构对可解释性的定义差异很大，尽管一些机构展示了有前景的内部实践和可视化工具，这些工具促进了工程团队与新闻编辑室之间的沟通。基于我们的分析，我们为新闻工程师和研究人员提供了关于如何在新闻个性化流程中采用可解释性方法的切实可行的实用指南。
英文摘要：
Research on explainability in recommender systems largely centers on end users, overlooking the perspectives of those who build and maintain these systems and their potential use cases such as model debugging. In this study, we examine how news engineers and related technical stakeholders perceive and implement personalization and explainability in practice. We conducted 15 semi-structured interviews across nine news organizations, spanning diverse regions in both public and private sectors, to investigate the challenges and motivations shaping their approaches. Our findings reveal that personalization is not always a straightforward or desirable choice for news organizations, as concerns around user tracking, editorial control, and resource constraints often limit its adoption. Even among organizations implementing personalized news recommender systems in production, explainability is rarely prioritized, with day-to-day operational demands frequently taking precedence over longer-term transparency goals. Definitions of explainability vary widely across organizations, though some demonstrate promising internal practices and visualization tools that facilitate communication between engineering teams and newsrooms. Based on our analysis, we provide actionable and practical guidelines for news engineers and researchers on how to adopt explainability methods within a news personalization pipeline.
