Do We Care About Personalization and Explainability? An Interview Study with News Recommendation Engineers

2026-09-18

AI Summary

研究通过访谈新闻工程师探讨个性化和可解释性在新闻推荐系统中的实践问题，揭示了实际挑战并提出实施指南。

Abstract

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.

Problem

• personalization
• explainability
• news recommendation
• stakeholders
• operational demands

Innovation

• personalization
• explainability
• news recommendation
• stakeholders' perspectives
• visualization tools