Personalized Visualization Recommendation

@article{Qian2021PersonalizedVR,
  title={Personalized Visualization Recommendation},
  author={Xin-Yao Qian and Ryan A. Rossi and Fan Du and Sungchul Kim and Eunyee Koh and Sana Malik and Tak Yeon Lee and Nesreen K. Ahmed},
  journal={ACM Transactions on the Web (TWEB)},
  year={2021},
  volume={16},
  pages={1 - 47}
}
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… 

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References

SHOWING 1-10 OF 88 REFERENCES

Learning to Recommend Visualizations from Data

This work proposes the first end-to-end ML-based visualization recommendation system that leverages a large corpus of datasets and their relevant visualizations to learn a visualization recommendation model automatically and recommends more effective and useful visualizations compared to existing state-of-the-art rule-based systems.

SeeDB: Efficient Data-Driven Visualization Recommendations to Support Visual Analytics

This work proposes SeeDB, a visualization recommendation engine to facilitate fast visual analysis: given a subset of data to be studied, SeeDB intelligently explores the space of visualizations, evaluates promising visualizations for trends, and recommends those it deems most β€œuseful” or β€œinteresting”.

VizML: A Machine Learning Approach to Visualization Recommendation

A novel machine learning-based approach to visualization recommendation that learns visualization design choices from a large corpus of datasets and associated visualizations that is comparable to human performance when predicting consensus visualization type and exceeds that of other visualization recommender systems.

An Automated Approach to Reasoning About Task-Oriented Insights in Responsive Visualization

An automated approach to approximating the loss of support for task-oriented visualization insights (identification, comparison, and trend) in responsive transformation of a source visualization is proposed.

Towards a general-purpose query language for visualization recommendation

This paper presents the preliminary design of CompassQL, which defines a partial specification that describes enumeration constraints, and methods for choosing, ranking, and grouping recommended visualizations in a specification language for querying over the space of visualizations.

Understanding User Behavior For Document Recommendation

A large-scale log study of users’ interaction behavior with the explainable recommendation on one of the largest cloud document platforms office.com suggests opportunities to improve explanations and more generally the design of systems that provide and explain recommendations for documents.

Voyager: Exploratory Analysis via Faceted Browsing of Visualization Recommendations

It is found that Voyager facilitates exploration of previously unseen data and leads to increased data variable coverage, and the need to balance rapid exploration and targeted question-answering for visualization tools is distill.

Foresight: Recommending Visual Insights

Foresight is introduced, a system that helps the user rapidly discover visual insights from large high-dimensional datasets by providing "global" views of insight space to help orient the user and ensure a thorough exploration process.

VizNet: Towards A Large-Scale Visualization Learning and Benchmarking Repository

VizNet is presented: a large-scale corpus of over 31 million datasets compiled from open data repositories and online visualization galleries that provides the necessary common baseline for comparing visualization design techniques, and developing benchmark models and algorithms for automating visual analysis.

Dziban: Balancing Agency & Automation in Visualization Design via Anchored Recommendations

Dziban is a visualization API that supports both ambiguous specification and a novel anchoring mechanism for conveying desired context, providing a more concise and flexible authoring experience through automated design, while preserving predictability and control through anchored recommendations.
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