Agnostic Visual Recommendation Systems: Open Challenges and Future Directions
arXiv:2302.00569 · doi:10.1109/TVCG.2024.3374571
Abstract
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.
16 pages, 4 figures
References in corpus (13)
- Hierarchical Text-Conditional Image Generation with CLIP Latents
- Deep Learning based Recommender System: A Survey and New Perspectives
- A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT
- A Survey on ML4VIS: Applying Machine Learning Advances to Data Visualization
- KG4Vis: A Knowledge Graph-Based Approach for Visualization Recommendation
- Table2Charts: Recommending Charts by Learning Shared Table Representations
- PI2: End-to-end Interactive Visualization Interface Generation from Queries
- Visual Data Analysis with Task-based Recommendations
- Visually-Aware Personalized Recommendation using Interpretable Image Representations
- GREASE: Generate Factual and Counterfactual Explanations for GNN-based Recommendations
- Are Metrics Enough? Guidelines for Communicating and Visualizing Predictive Models to Subject Matter Experts
- NL2INTERFACE: Interactive Visualization Interface Generation from Natural Language Queries
- SlimPajama-DC: Understanding Data Combinations for LLM Training