# Personalized Visualization Recommendation

arXiv: https://arxiv.org/abs/2102.06343 · HTML: https://mlsanity.com/paper/2102.06343/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 90 references that are in the map.
- First posted on arXiv 12 Feb 2021.

## Builds on (most cited first, 14 of 14)

- [Neural Collaborative Filtering](https://mlsanity.com/paper/1708.05031/) (2017)
- [Fast Matrix Factorization for Online Recommendation with Implicit Feedback](https://mlsanity.com/paper/1708.05024/) (2017)
- [Data2Vis: Automatic Generation of Data Visualizations Using Sequence to Sequence Recurrent Neural Networks](https://mlsanity.com/paper/1804.03126/) (2018)
- [CATN: Cross-Domain Recommendation for Cold-Start Users via Aspect Transfer Network](https://mlsanity.com/paper/2005.10549/) (2020)
- [VizML: A Machine Learning Approach to Visualization Recommendation](https://mlsanity.com/paper/1808.04819/) (2018)
- [Graph Enhanced Representation Learning for News Recommendation](https://mlsanity.com/paper/2003.14292/) (2020)
- [VizNet: Towards A Large-Scale Visualization Learning and Benchmarking Repository](https://mlsanity.com/paper/1905.04616/) (2019)
- [Effortless Data Exploration with zenvisage: An Expressive and Interactive Visual Analytics System](https://mlsanity.com/paper/1604.03583/) (2016)
- [DataSite: Proactive Visual Data Exploration with Computation of Insight-based Recommendations](https://mlsanity.com/paper/1802.08621/) (2018)
- [Attentive Aspect Modeling for Review-aware Recommendation](https://mlsanity.com/paper/1811.04375/) (2018)
- [Foresight: Recommending Visual Insights](https://mlsanity.com/paper/1707.03877/) (2017)
- [A Scalable Hybrid Research Paper Recommender System for Microsoft Academic](https://mlsanity.com/paper/1905.08880/) (2019)
- [ML-based Visualization Recommendation: Learning to Recommend Visualizations from Data](https://mlsanity.com/paper/2009.12316/) (2020)
- [Columnwise Element Selection for Computationally Efficient Nonnegative Coupled Matrix Tensor Factorization](https://mlsanity.com/paper/2003.03506/) (2020)

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

- [HAIChart: Human and AI Paired Visualization System](https://mlsanity.com/paper/2406.11033/) (2024)
- [Insight-centric Visualization Recommendation](https://mlsanity.com/paper/2103.11297/) (2021)

## Abstract (arXiv)

Visualization recommendation work has focused solely on scoring visualizations based on the underlying dataset and not the actual user and their past visualization feedback. These systems recommend the same visualizations for every user, despite that the underlying user interests, intent, and visualization preferences are likely to be fundamentally different, yet vitally important. In this work, we formally introduce the problem of personalized visualization recommendation and present a generic learning framework for solving it. In particular, we focus on recommending visualizations personalized for each individual user based on their past visualization interactions (e.g., viewed, clicked, manually created) along with the data from those visualizations. More importantly, the framework can learn from visualizations relevant to other users, even if the visualizations are generated from completely different datasets. Experiments demonstrate the effectiveness of the approach as it leads to higher quality visualization recommendations tailored to the specific user intent and preferences. To support research on this new problem, we release our user-centric visualization corpus consisting of 17.4k users exploring 94k datasets with 2.3 million attributes and 32k user-generated visualizations.