activity
20202025
most citedML-based Visualization Recommendation: Learning to Recommend Visualizations from Data

11 citations · 25 across the 6 of their papers we have counts for

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5 papers · 1 filter

cs.HC2025

Chart-to-Experience: Benchmarking Multimodal LLMs for Predicting Experiential Impact of Charts

Seon Gyeom Kim, Jae Young Choi, Ryan Rossi +2

The field of Multimodal Large Language Models (MLLMs) has made remarkable progress in visual understanding tasks, presenting a vast opportunity to predict the perceptual and emotio…

cs.HC2024

Optimizing Data Delivery: Insights from User Preferences on Visuals, Tables, and Text

Reuben Luera, Ryan Rossi, Franck Dernoncourt +9

In this work, we research user preferences to see a chart, table, or text given a question asked by the user. This enables us to understand when it is best to show a chart, table,…

cs.HC2024

Understanding the Impact of Spatial Immersion in Web Data Stories

Seon Gyeom Kim, Juhyeong Park, Yutaek Song +6

An increasing number of web articles engage the reader with the feeling of being immersed in the data space. However, the exact characteristics of spatial immersion in the context…

cs.HC20214 cited

An Evaluation-Focused Framework for Visualization Recommendation Algorithms

Zehua Zeng, Phoebe Moh, Fan Du +5

Although we have seen a proliferation of algorithms for recommending visualizations, these algorithms are rarely compared with one another, making it difficult to ascertain which a…

cs.HC202110 cited

Insight-centric Visualization Recommendation

Camille Harris, Ryan A. Rossi, Sana Malik +5

Visualization recommendation systems simplify exploratory data analysis (EDA) and make understanding data more accessible to users of all skill levels by automatically generating v…