User models for recommender systems (RecSys) typically assume stable preferences, similarity-based relevance, and session-bounded interactions -- assumptions derived from high-volume consumer contexts. This paper investigates these assumptions for humanities scholars working with digital archives. Following a human-centered design approach, we conducted focus groups and analyzed interview data from 18 researchers. Our analysis identifies four dimensions where scholarly information-seeking diverges from common RecSys user modeling: (1) context volatility -- preferences shift with research tasks and domain expertise; (2) epistemic trust -- relevance depends on verifiable provenance; (3) contrastive seeking -- researchers seek items that challenge their current direction; and (4) strand continuity -- research spans long-term threads rather than discrete sessions. We discuss implications for user modeling and outline how these dimensions relate to collaborative filtering, content-based, and session-based recommendation. We propose these dimensions as a diagnostic framework applicable beyond archives to similar application domains where typical user modeling assumptions may not hold.

翻译：推荐系统（RecSys）的用户模型通常假设稳定的偏好、基于相似性的相关性以及会话界定的交互——这些假设源自高用户量消费场景。本文针对数字档案中的人文学者研究这些假设的适用性。采用以人为中心的设计方法，我们组织了焦点小组并分析了18位研究人员的访谈数据。分析揭示了人文学者信息检索与常见RecSys用户模型存在差异的四个维度：（1）情境波动性——偏好随研究任务和领域专业知识而变化；（2）认知信任——相关性依赖于可验证的来源；（3）对比性检索——研究者寻求挑战当前研究方向的资料；（4）线索连续性——研究贯穿长期线索而非独立会话。我们探讨了对用户模型的影响，并概述了这些维度与协同过滤、基于内容和基于会话的推荐之间的关系。我们将这些维度视为诊断框架，可适用于档案之外的类似应用领域，其中典型的用户模型假设可能不成立。