Visualization for Recommendation Explainability: A Survey and New Perspectives

• URL: http://arxiv.org/abs/2305.11755v3
• Date: Wed, 5 Jun 2024 14:41:02 GMT
• Title: Visualization for Recommendation Explainability: A Survey and New Perspectives
• Authors: Mohamed Amine Chatti, Mouadh Guesmi, Arham Muslim,
• Abstract summary: We systematically review the literature on explanations in recommender systems based on four dimensions. We derive a set of guidelines that might be constructive for designing explanatory visualizations in recommender systems. The aim of this review is to help recommendation researchers and practitioners better understand the potential of visually explainable recommendation research.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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 result, we derive a set of guidelines that might be constructive for designing explanatory visualizations in recommender systems and identify perspectives for future work in this field. The aim of this review is to help recommendation researchers and practitioners better understand the potential of visually explainable recommendation research and to support them in the systematic design of visual explanations in current and future recommender systems.

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