Adapting user interface designs for specific tasks performed by different users is a challenging yet important problem. Automatically adapting visualization designs to users and contexts (e.g., tasks, display devices, environments, etc.) can theoretically improve human–computer interaction to acquire insights from complex datasets. However, effectiveness of any specific visualization is moderated by individual differences in knowledge, skills, and abilities for different contexts. A modeling framework called P ersonalized R ecommender System for I nformation visualization M ethods via E xtended matrix completion (PRIME) is proposed for recommending the optimal visualization designs for individual users in different contexts. PRIME quantitatively models covariates (e.g., psychological and behavioral measurements) to predict recommendation scores (e.g., perceived complexity, mental workload, etc.) for users to adapt the visualization specific to the context. An evaluation study was conducted and showed that PRIME can achieve satisfactory recommendation accuracy for adapting visualization, even when there are limited historical data. PRIME can make accurate recommendations even for new users or new tasks based on historical wearable sensor signals and recommendation scores. This capability contributes to designing a new generation of visualization systems that will adapt to users’ states. PRIME can support researchers in reducing the sample size requirements to quantify individual differences, and practitioners in adapting visualizations according to user states and contexts.

中文翻译：

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PRIME：通过扩展矩阵完成的信息可视化方法的个性化推荐系统

为不同用户执行的特定任务调整用户界面设计是一个具有挑战性但重要的问题。自动使可视化设计适应用户和上下文（例如，任务、显示设备、环境等），理论上可以改善人机交互，从而从复杂的数据集中获取洞察力。但是，任何特定可视化的有效性都会受到不同背景下知识、技能和能力的个体差异的影响。一个名为的建模框架磷个性化R电子商务系统一世信息可视化米方法通过乙提出了扩展矩阵完成（PRIME），用于为不同上下文中的个人用户推荐最佳可视化设计。PRIME 对协变量（例如，心理和行为测量）进行定量建模，以预测推荐分数（例如，感知复杂性、心理工作量等），以便用户调整特定于上下文的可视化。一项评估研究表明，即使在历史数据有限的情况下，PRIME 在适应可视化方面也能达到令人满意的推荐准确性。PRIME 甚至可以根据历史可穿戴传感器信号和推荐分数为新用户或新任务做出准确的推荐。这种能力有助于设计适应用户状态的新一代可视化系统。