Visualization for Recommendation Explainability: A Survey and New Perspectives
Mohamed Amine Chatti, Mouadh Guesmi, Arham Muslim

TL;DR
This paper reviews the role of visualization in explainable recommender systems, offering a comprehensive overview of current research, guidelines for designing visual explanations, and future research directions.
Contribution
It provides a systematic review of visual explanation techniques in recommender systems and proposes guidelines for designing effective explanatory visualizations.
Findings
Identified key dimensions of explanations: goal, scope, style, format.
Derived guidelines for designing visual explanations.
Highlighted future research perspectives in visual explainability.
Abstract
Providing system-generated explanations for recommendations represents an important step towards transparent and trustworthy recommender systems. Explainable recommender systems provide a human-understandable rationale for their outputs. Over the last two decades, explainable recommendation has attracted much attention in the recommender systems research community. This paper aims to provide a comprehensive review of research efforts on visual explanation in recommender systems. More concretely, we systematically review the literature on explanations in recommender systems based on four dimensions, namely explanation goal, explanation scope, explanation style, and explanation format. Recognizing the importance of visualization, we approach the recommender system literature from the angle of explanatory visualizations, that is using visualizations as a display style of explanation. As a…
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Taxonomy
TopicsData Visualization and Analytics · Image and Video Quality Assessment · Explainable Artificial Intelligence (XAI)
