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

TL;DR

This paper explores how user traits influence preferences for visual, tabular, or textual data representations and investigates the potential of large language models to replicate these preferences for improved data tool personalization.

Contribution

It provides new insights into the impact of user characteristics on data presentation preferences and demonstrates how LLMs can be used to emulate individual user choices.

Findings

User traits significantly influence preferred data formats.

LLMs can partially replicate individual user preferences.

Personalized data tools can enhance user experience.

Abstract

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, or text to the user for the specific question. For this, we conduct a user study where users are shown a question and asked what they would prefer to see and used the data to establish that a user's personal traits does influence the data outputs that they prefer. Understanding how user characteristics impact a user's preferences is critical to creating data tools with a better user experience. Additionally, we investigate to what degree an LLM can be used to replicate a user's preference with and without user preference data. Overall, these findings have significant implications pertaining to the development of data tools and the replication of human preferences using LLMs. Furthermore, this work demonstrates the potential use of LLMs to replicate user preference data which has major implications for future user modeling and personalization research.