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

• URL: http://arxiv.org/abs/2411.07451v1
• Date: Tue, 12 Nov 2024 00:24:31 GMT
• Title: Optimizing Data Delivery: Insights from User Preferences on Visuals, Tables, and Text
• Authors: Reuben Luera, Ryan Rossi, Franck Dernoncourt, Alexa Siu, Sungchul Kim, Tong Yu, Ruiyi Zhang, Xiang Chen, Nedim Lipka, Zhehao Zhang, Seon Gyeom Kim, Tak Yeon Lee,
• Abstract summary: We conduct a user study where users are shown a question and asked what they would prefer to see. We use the data to establish that a user's personal traits does influence the data outputs that they prefer.
• Score: 59.68239795065175
• License:
• 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.

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