根据用户偏好优化数据呈现方式，提升信息获取效率。
Optimizing Data Delivery: Insights from User Preferences on Visuals, Tables, and Text
- 通过用户实验分析图表、表格、文字的偏好选择机制。
- 发现用户个人特质显著影响对数据形式的偏好。
- 验证大模型可有效模拟用户偏好，助力个性化推荐。
本文研究用户在面对问题时对图表、表格或文本的偏好，以确定在特定情境下最合适的呈现形式。通过用户实验，我们发现用户个人特征显著影响其偏好的数据输出形式。理解用户特质如何影响偏好，对设计更优用户体验的数据工具至关重要。此外，我们评估了大语言模型（LLM）在有无用户偏好数据的情况下复制用户偏好的能力。结果表明，大模型具备模拟用户偏好的潜力，这对未来用户建模与个性化研究具有重要意义。
原文摘要 · Abstract (English)
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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