f-RecX: A framework for designing effective textual explanations in recommender systems’ user interfaces

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.