为人文学者设计更精准的档案推荐系统，突破传统用户模型假设。
What Do Humanities Scholars Need? A User Model for Recommendation in Digital Archives
- 基于18位学者访谈，发现学术搜索与普通推荐场景差异显著。
- 提出四维新框架：情境波动、认知信任、对比探索、长期线索延续。
- 适合数字人文、档案系统研究者，推动个性化推荐适配学术场景。
推荐系统用户模型通常假设偏好稳定、相关性基于相似性、交互行为局限于单次会话——这些假设源于高流量消费场景。本文通过人本设计方法，对18位从事数字档案研究的人文学者开展焦点小组与深度访谈。分析揭示了四个学术信息检索与通用推荐模型存在差异的维度：（1）情境波动性——偏好随研究任务与专业背景动态变化；（2）认知信任——相关性依赖可验证的来源可信度；（3）对比性探索——研究者主动寻求挑战现有方向的内容；（4）线索连续性——研究呈长期性线索延伸，而非离散会话。论文讨论了这些发现对用户建模的影响，阐释其与协同过滤、内容基础及会话式推荐的关系，并提出该四维框架作为诊断工具，可推广至其他不适用常规用户建模的应用领域。
原文摘要 · Abstract (English)
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.
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