What Do Humanities Scholars Need? A User Model for Recommendation in Digital Archives

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.