# MultiVision: Designing Analytical Dashboards with Deep Learning Based Recommendation

arXiv: https://arxiv.org/abs/2107.07823 · HTML: https://mlsanity.com/paper/2107.07823/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jul 2021.

## Built on by (most cited first, 11 of 11)

- [HAIChart: Human and AI Paired Visualization System](https://mlsanity.com/paper/2406.11033/) (2024)
- [LightVA: Lightweight Visual Analytics with LLM Agent-Based Task Planning and Execution](https://mlsanity.com/paper/2411.05651/) (2024)
- [Telling Stories from Computational Notebooks: AI-Assisted Presentation Slides Creation for Presenting Data Science Work](https://mlsanity.com/paper/2203.11085/) (2022)
- [Agnostic Visual Recommendation Systems: Open Challenges and Future Directions](https://mlsanity.com/paper/2302.00569/) (2023)
- [Automatic Generation of Conversational Interfaces for Tabular Data Analysis](https://mlsanity.com/paper/2305.11326/) (2023)
- [Adversarial Attacks on Machine Learning-Aided Visualizations](https://mlsanity.com/paper/2409.02485/) (2024)
- [MDSF: Context-Aware Multi-Dimensional Data Storytelling Framework based on Large language Model](https://mlsanity.com/paper/2501.01014/) (2025)
- [DashChat: Interactive Authoring of Performance Dashboard Design Prototypes through Conversation with LLM-Powered Agent](https://mlsanity.com/paper/2504.12865/) (2025)
- [Feature-Action Design Patterns for Storytelling Visualizations with Time Series Data](https://mlsanity.com/paper/2402.03116/) (2024)
- [Crystalis: Progressive Nucleation and Semantic Annealing for Coordinated Multi-View Visualization Generation](https://mlsanity.com/paper/2607.24766/) (2026)
- [DashArena: Benchmarking LLMs on Interactive Analytic Dashboard Generation](https://mlsanity.com/paper/2608.10567/) (2026)

## Abstract (arXiv)

We contribute a deep-learning-based method that assists in designing analytical dashboards for analyzing a data table. Given a data table, data workers usually need to experience a tedious and time-consuming process to select meaningful combinations of data columns for creating charts. This process is further complicated by the need of creating dashboards composed of multiple views that unveil different perspectives of data. Existing automated approaches for recommending multiple-view visualizations mainly build on manually crafted design rules, producing sub-optimal or irrelevant suggestions. To address this gap, we present a deep learning approach for selecting data columns and recommending multiple charts. More importantly, we integrate the deep learning models into a mixed-initiative system. Our model could make recommendations given optional user-input selections of data columns. The model, in turn, learns from provenance data of authoring logs in an offline manner. We compare our deep learning model with existing methods for visualization recommendation and conduct a user study to evaluate the usefulness of the system.