十五年后，推荐系统仍困在旧范式中，亟需根本性反思。
We're Still Doing It (All) Wrong: Recommender Systems, Fifteen Years Later
- 重审2011年批评，指出方法论与认知基础问题持续存在
- 揭示复杂度提升掩盖了评估与可复现性的深层缺陷
- 呼吁以人文关怀和可持续实践重构研究目标
2011年，Xavier Amatriain曾警示：推荐系统研究“全错了”。其批判基于统计误读与方法捷径，至今依然切中要害。然而，我们并未修正方向，反而在相同脆弱基础上叠加了更多复杂性。本文重提这一诊断，指出概念、认识论与基础设施的缺陷仍在以更隐蔽或系统化形式延续。结合近期关于可复现性、评估方法、环境影响与参与式设计的研究，我们展示领域复杂度的加速增长已超越自我反思能力。文中介绍社区推动的范式转变努力，包括研讨会、评估框架及对价值敏感与参与式研究的呼吁。但真正的变革不仅需新指标或更好工具，更需重新定义推荐系统研究的目的、服务对象与知识生产方式。我们的呼吁不仅是技术改革，更是建立于认识谦逊、人类影响与可持续实践之上的研究新范式。
原文摘要 · Abstract (English)
In 2011, Xavier Amatriain sounded the alarm: recommender systems research was "doing it all wrong" [1]. His critique, rooted in statistical misinterpretation and methodological shortcuts, remains as relevant today as it was then. But rather than correcting course, we added new layers of sophistication on top of the same broken foundations. This paper revisits Amatriain's diagnosis and argues that many of the conceptual, epistemological, and infrastructural failures he identified still persist, in more subtle or systemic forms. Drawing on recent work in reproducibility, evaluation methodology, environmental impact, and participatory design, we showcase how the field's accelerating complexity has outpaced its introspection. We highlight ongoing community-led initiatives that attempt to shift the paradigm, including workshops, evaluation frameworks, and calls for value-sensitive and participatory research. At the same time, we contend that meaningful change will require not only new metrics or better tooling, but a fundamental reframing of what recommender systems research is for, who it serves, and how knowledge is produced and validated. Our call is not just for technical reform, but for a recommender systems research agenda grounded in epistemic humility, human impact, and sustainable practice.
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