# Visualization for Recommendation Explainability: A Survey and New Perspectives

arXiv: https://arxiv.org/abs/2305.11755 · HTML: https://mlsanity.com/paper/2305.11755/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 168 references that are in the map.
- First posted on arXiv 19 May 2023.

## Builds on (most cited first, 26 of 26)

- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [Explanation in Artificial Intelligence: Insights from the Social Sciences](https://mlsanity.com/paper/1706.07269/) (2017)
- [Questioning the AI: Informing Design Practices for Explainable AI User Experiences](https://mlsanity.com/paper/2001.02478/) (2020)
- [Explainable Recommendation: A Survey and New Perspectives](https://mlsanity.com/paper/1804.11192/) (2018)
- [A Survey on Conversational Recommender Systems](https://mlsanity.com/paper/2004.00646/) (2020)
- [Making deep neural networks right for the right scientific reasons by interacting with their explanations](https://mlsanity.com/paper/2001.05371/) (2020)
- [Visual Analytics in Deep Learning: An Interrogative Survey for the Next Frontiers](https://mlsanity.com/paper/1801.06889/) (2018)
- [explAIner: A Visual Analytics Framework for Interactive and Explainable Machine Learning](https://mlsanity.com/paper/1908.00087/) (2019)
- [One Explanation Does Not Fit All: The Promise of Interactive Explanations for Machine Learning Transparency](https://mlsanity.com/paper/2001.09734/) (2020)
- [Do Convolutional Neural Networks Learn Class Hierarchy?](https://mlsanity.com/paper/1710.06501/) (2017)
- [RetainVis: Visual Analytics with Interpretable and Interactive Recurrent Neural Networks on Electronic Medical Records](https://mlsanity.com/paper/1805.10724/) (2018)
- [CNN Explainer: Learning Convolutional Neural Networks with Interactive Visualization](https://mlsanity.com/paper/2004.15004/) (2020)
- [A systematic review and taxonomy of explanations in decision support and recommender systems](https://mlsanity.com/paper/2006.08672/) (2020)
- [GAN Lab: Understanding Complex Deep Generative Models using Interactive Visual Experimentation](https://mlsanity.com/paper/1809.01587/) (2018)
- [Visual Analytics for Explainable Deep Learning](https://mlsanity.com/paper/1804.02527/) (2018)
- [Towards Better Analysis of Machine Learning Models: A Visual Analytics Perspective](https://mlsanity.com/paper/1702.01226/) (2017)
- [ViCE: Visual Counterfactual Explanations for Machine Learning Models](https://mlsanity.com/paper/2003.02428/) (2020)
- [The State of the Art in Enhancing Trust in Machine Learning Models with the Use of Visualizations](https://mlsanity.com/paper/2212.11737/) (2022)
- [Direct-Manipulation Visualization of Deep Networks](https://mlsanity.com/paper/1708.03788/) (2017)
- [The State of the Art in Integrating Machine Learning into Visual Analytics](https://mlsanity.com/paper/1802.07954/) (2018)
- [Recent Research Advances on Interactive Machine Learning](https://mlsanity.com/paper/1811.04548/) (2018)
- [Explainable Outfit Recommendation with Joint Outfit Matching and Comment Generation](https://mlsanity.com/paper/1806.08977/) (2018)
- [Are we measuring trust correctly in explainability, interpretability, and transparency research?](https://mlsanity.com/paper/2209.00651/) (2022)
- [Do Social Explanations Work? Studying and Modeling the Effects of Social Explanations in Recommender Systems](https://mlsanity.com/paper/1304.3405/) (2013)
- [Effects of interactivity and presentation on review-based explanations for recommendations](https://mlsanity.com/paper/2105.11794/) (2021)
- [UniWalk: Explainable and Accurate Recommendation for Rating and Network Data](https://mlsanity.com/paper/1710.07134/) (2017)

## Built on by (most cited first, 2 of 2)

- [Visual or Textual: Effects of Explanation Format and Personal Characteristics on the Perception of Explanations in an Educational Recommender System](https://mlsanity.com/paper/2603.25624/) (2026)
- [Investigating the Effects of Different Levels of User Control in an Interactive Educational Recommender System](https://mlsanity.com/paper/2605.01400/) (2026)

## Abstract (arXiv)

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.