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

2026-04-02

AI Summary

This study addresses the limitations of conventional recommender systems, which assume stable user preferences and session-bound interactions, rendering them ill-suited for the complex and dynamic information needs of humanities scholars working with digital archives. Through focus groups and in-depth interviews with 18 humanities researchers, the authors identify four key discrepancies between humanistic information-seeking behaviors and standard recommendation models: contextual volatility, cognitive trustworthiness, comparative exploration, and continuity of research线索 (research thread continuity). Building on these insights, the paper proposes a four-dimensional user modeling paradigm tailored to low-frequency, high-expertise knowledge domains such as digital archives. This framework challenges the oversimplified behavioral assumptions of traditional recommender systems and offers both theoretical grounding and practical guidance for designing recommendation mechanisms that genuinely support humanities research practices.

Abstract

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.

Problem

• recommender systems
• user modeling
• digital archives
• humanities scholars
• information-seeking

Innovation

• user modeling
• digital archives
• humanities scholars
• context volatility
• epistemic trust