从交互到意图：从溯源日志中推断用户目标
From Interaction to Intent: Inferring User Objectives from Provenance Logs
AI总结：
研究能否用溯源日志对视觉探索任务中的用户意图分类，记录参与者交互，发现不同分析目标有不同行为特征，嵌入上下文信息可使分类器跨数据集和投影方法预测用户目标。
摘要：
从用户交互历史中自动推断分析意图的能力，可让交互式人工智能系统在探索性数据分析中主动帮助用户。本文研究溯源日志能否用于视觉探索任务中的用户意图分类。通过记录参与者在分析任务中与多维数据投影的交互，发现不同分析目标有不同行为特征，且嵌入上下文信息能使分类器跨数据集和投影方法预测用户目标，为意图感知可视化系统发展做贡献。
英文摘要：
The ability to automatically infer analytic intent from user interaction histories could enable interactive AI systems to proactively assist users during exploratory data analysis. In this paper, we examine whether provenance logs -- detailed records capturing sequences and timing of user interactions -- can be used to classify user intentions in visual exploration tasks. To investigate this, we record how participants interact with multiple multidimensional data projections across a range of analytic tasks, capturing fine-grained mouse interaction data throughout each session. We find that distinct behavioral signatures emerge across different analytic objectives. For instance, users examining properties of specific clusters exhibit markedly different interaction patterns compared to those searching for outliers. More importantly, we show that embedding contextual information into interaction provenance enables classifiers to predict user objectives that generalize across datasets and projection methods. These findings demonstrate that low-level interaction data can serve as a practical bridge to high-level analytic intent, contributing to the development of intent-aware visualization systems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
