Deconstructing Categorization in Visualization Recommendation: A Taxonomy and Comparative Study

by   Doris Jung-Lin Lee, et al.

Visualization recommendation (VisRec) systems provide users with suggestions for potentially interesting and useful next steps during exploratory data analysis. These recommendations are typically organized into categories based on their analytical actions, i.e., operations employed to transition from the current exploration state to a recommended visualization. However, despite the emergence of a plethora of VisRec systems in recent work, the utility of the categories employed by these systems in analytical workflows has not been systematically investigated. Our paper explores the efficacy of recommendation categories by formalizing a taxonomy of common categories and developing a system, Frontier, that implements these categories. Using Frontier, we evaluate workflow strategies adopted by users and how categories influence those strategies. Participants found recommendations that add attributes to enhance the current visualization and recommendations that filter to sub-populations to be comparatively most useful during data exploration. Our findings pave the way for next-generation VisRec systems that are adaptive and personalized via carefully chosen, effective recommendation categories.



There are no comments yet.


page 10


Vis Ex Machina: An Analysis of Trust in Human versus Algorithmically Generated Visualization Recommendations

More visualization systems are simplifying the data analysis process by ...

Visualization in Astrophysics: Developing New Methods, Discovering Our Universe, and Educating the Earth

We present a state-of-the-art report on visualization in astrophysics. W...

Recommendations for Visualization Recommendations: Exploring Preferences and Priorities in Public Health

The promise of visualization recommendation systems is that analysts wil...

Learning Hierarchical Item Categories from Implicit Feedback Data for Efficient Recommendations and Browsing

Searching, browsing, and recommendations are common ways in which the "c...

VisMaker: a Question-Oriented Visualization Recommender System for Data Exploration

The increasingly rapid growth of data production and the consequent need...

Generating recommendations for entity-oriented exploratory search

We introduce the task of recommendation set generation for entity-orient...

Reviewing Data Visualization: an Analytical Taxonomical Study

This paper presents an analytical taxonomy that can suitably describe, r...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.