F-RecX
A framework for designing effective textual explanations in recommender systems’ user interfaces
Bibliographic Data
| ID | 21642629 |
|---|---|
| Authors | Ibrahim Al-Hazwani (0000-0003-1873-104X, University of Zurich, corresponding author), Gabriela Morgenshtern (0000-0003-4762-8797, University of Zurich), Mennatallah El‐Assady (0000-0001-8526-2613, ETH Zurich), Mennatallah El-Assady, Jürgen Bernard (0000-0001-8741-9709, University of Zurich) |
| Year | 2025 |
| Volume | 205 |
| Pages | 103627 |
| Publication date | 2025-11-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | International Journal of Human-Computer Studies (JOURNAL) |
| Journal identifiers | ISSN: 1071-5819 • E-ISSN: 1095-9300 |
| Publisher | Elsevier BV (PUBLISHER) |
| DOI | 10.1016/j.ijhcs.2025.103627 |
| OpenAlex | W4414445669 |
| Language | EN |
| References cited | 39 |
Recommender systems (RecSys) have become ubiquitous in users’ daily digital interactions, significantly influencing decision-making processes. As these systems grow in algorithmic complexity, effective explanations for non-expert users become essential to fostering understanding and trust. While academic research explores diverse explanation methods, commercial applications predominantly employ textual explanations due to their implementation efficiency and user familiarity. However, the effectiveness of these textual explanations is often compromised by suboptimal presentation within RecSys user interfaces (UIs), leading to reduced user engagement and comprehension. This issue is particularly relevant given the recent emergence of large language models (LLMs) for generating RecSys explanations. We introduce f-RecX, a conceptual framework for characterizing and designing effective textual explanations in RecSys UIs. Based on a two-phase methodology combining qualitative user studies and quantitative evaluations, f-RecX maps four input dimensions (Explanation Style, Goals, Domain Dynamics, and Recommender Systems Technique) to an output dimension focused on visual representation. The framework aims to enhance the’consumability’ of textual explanations by making them easier to locate and comprehend, and more valuable for non-expert users. We demonstrate f-RecX’s applicability through a usage scenario and analysis of existing RecSys UIs, offering valuable insights for enhancing explainability and user experience. • f-RecX uniquely bridges algorithmic and human-centered design by integrating four input dimensions (Explanation Style, Goals, Domain Dynamics, and Recommender Techniques) with visual presentation parameters, addressing the gap between explanation content generation and effective UI implementation. • The framework introduces the concept of explanation ”consumability” - how easily users can locate, understand, and derive value from explanations - providing empirically validated visual design guidelines including optimal font sizes (24-32px), positioning (top-left), and explanation length ( 20 words). • Through a comprehensive two-phase methodology combining empathy workshops and quantitative surveys, f-RecX provides design guidance that demonstrates how visual characteristics significantly impact explanation effectiveness, particularly relevant for LLM-generated explanations in commercial applications
Conceptual framework · Recommender system · User experience design · User interface · Explainable Artificial Intelligence (XAI · Recommender Systems and Techniques · Topic Modeling
| Citation velocity | historical |
|---|---|
| Highly cited | No |