# Agnostic Visual Recommendation Systems: Open Challenges and Future Directions

arXiv: https://arxiv.org/abs/2302.00569 · HTML: https://mlsanity.com/paper/2302.00569/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 101 references that are in the map.
- First posted on arXiv 1 Feb 2023.

## Builds on (most cited first, 25 of 25)

- [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://mlsanity.com/paper/2204.06125/) (2022)
- [GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models](https://mlsanity.com/paper/2112.10741/) (2021)
- [Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges](https://mlsanity.com/paper/2103.11251/) (2021)
- [A Survey on Neural Network Interpretability](https://mlsanity.com/paper/2012.14261/) (2020)
- [Deformable ProtoPNet: An Interpretable Image Classifier Using Deformable Prototypes](https://mlsanity.com/paper/2111.15000/) (2021)
- [Data2Vis: Automatic Generation of Data Visualizations Using Sequence to Sequence Recurrent Neural Networks](https://mlsanity.com/paper/1804.03126/) (2018)
- [VizML: A Machine Learning Approach to Visualization Recommendation](https://mlsanity.com/paper/1808.04819/) (2018)
- [AI4VIS: Survey on Artificial Intelligence Approaches for Data Visualization](https://mlsanity.com/paper/2102.01330/) (2021)
- [VizNet: Towards A Large-Scale Visualization Learning and Benchmarking Repository](https://mlsanity.com/paper/1905.04616/) (2019)
- [A Survey on ML4VIS: Applying Machine Learning Advances to Data Visualization](https://mlsanity.com/paper/2012.00467/) (2020)
- [GREASE: Generate Factual and Counterfactual Explanations for GNN-based Recommendations](https://mlsanity.com/paper/2208.04222/) (2022)
- [KG4Vis: A Knowledge Graph-Based Approach for Visualization Recommendation](https://mlsanity.com/paper/2107.12548/) (2021)
- [Table2Charts: Recommending Charts by Learning Shared Table Representations](https://mlsanity.com/paper/2008.11015/) (2020)
- [MultiVision: Designing Analytical Dashboards with Deep Learning Based Recommendation](https://mlsanity.com/paper/2107.07823/) (2021)
- [Foresight: Recommending Visual Insights](https://mlsanity.com/paper/1707.03877/) (2017)
- [PI2: End-to-end Interactive Visualization Interface Generation from Queries](https://mlsanity.com/paper/2107.08203/) (2021)
- [DashBot: Insight-Driven Dashboard Generation Based on Deep Reinforcement Learning](https://mlsanity.com/paper/2208.01232/) (2022)
- [NL2INTERFACE: Interactive Visualization Interface Generation from Natural Language Queries](https://mlsanity.com/paper/2209.08834/) (2022)
- [Evolving Context-Aware Recommender Systems With Users in Mind](https://mlsanity.com/paper/2007.15409/) (2020)
- [ML-based Visualization Recommendation: Learning to Recommend Visualizations from Data](https://mlsanity.com/paper/2009.12316/) (2020)
- [Visual Data Analysis with Task-based Recommendations](https://mlsanity.com/paper/2205.03183/) (2022)
- [Visually-Aware Personalized Recommendation using Interpretable Image Representations](https://mlsanity.com/paper/1806.09820/) (2018)
- [Are Metrics Enough? Guidelines for Communicating and Visualizing Predictive Models to Subject Matter Experts](https://mlsanity.com/paper/2205.05749/) (2022)
- [A Review and Collation of Graphical Perception Knowledge for Visualization Recommendation](https://mlsanity.com/paper/2109.01271/) (2021)
- [Beyond Heuristics: Learning Visualization Design](https://mlsanity.com/paper/1807.06641/) (2018)

## Built on by (most cited first, 4 of 4)

- [LIDA: A Tool for Automatic Generation of Grammar-Agnostic Visualizations and Infographics using Large Language Models](https://mlsanity.com/paper/2303.02927/) (2023)
- [Vi(E)va LLM! A Conceptual Stack for Evaluating and Interpreting Generative AI-based Visualizations](https://mlsanity.com/paper/2402.02167/) (2024)
- [DeepVIS: Bridging Natural Language and Data Visualization Through Step-wise Reasoning](https://mlsanity.com/paper/2508.01700/) (2025)
- [V-RECS, a Low-Cost LLM4VIS Recommender with Explanations, Captioning and Suggestions](https://mlsanity.com/paper/2406.15259/) (2024)

## Abstract (arXiv)

Visualization Recommendation Systems (VRSs) are a novel and challenging field of study aiming to help generate insightful visualizations from data and support non-expert users in information discovery. Among the many contributions proposed in this area, some systems embrace the ambitious objective of imitating human analysts to identify relevant relationships in data and make appropriate design choices to represent these relationships with insightful charts. We denote these systems as "agnostic" VRSs since they do not rely on human-provided constraints and rules but try to learn the task autonomously. Despite the high application potential of agnostic VRSs, their progress is hindered by several obstacles, including the absence of standardized datasets to train recommendation algorithms, the difficulty of learning design rules, and defining quantitative criteria for evaluating the perceptual effectiveness of generated plots. This paper summarizes the literature on agnostic VRSs and outlines promising future research directions.