Understanding visual complexity as it relates to websites has been an emergent area for many years. However, predicting the visual complexity of a website as perceived by users has been a real challenge. Perception is important because it influences user engagement, dictating if they will find it dull, engaging, or too complex. While others have suggested solutions to certain levels of success, here we propose a simple but accurate model that generates a Visual Complexity Score (VCS) based on common aspects of an HTML Document Object Model (DOM). We created our model based on a statistical analysis of 3300 ratings of 55 users on 30 web pages. We then implemented this prediction model in an open source Eclipse framework called ViCRAM that both predicts and visualises the complexity of web pages in the form of a pixelated heat map. Finally, we evaluated this model and the tool prediction with another user study of 6240 ratings of 104 users on 30 web pages. This study shows that our tool can predict the perceived complexity with a strong correlation to users’ perceived complexity.

多年来，了解与网站相关的视觉复杂性已经成为一个新兴领域。但是，预测用户感知到的网站的视觉复杂性是一个真正的挑战。感知很重要，因为它会影响用户的参与度，从而决定用户是否会觉得乏味，参与度或过于复杂。尽管其他人已经提出了针对某些成功级别的解决方案，但在这里我们提出了一个简单而准确的模型，该模型基于HTML文档对象模型（DOM）的常见方面生成可视化复杂度评分（VCS）。我们基于对30个网页上55个用户的3300个评分的统计分析，创建了我们的模型。然后，我们在名为ViCRAM的开源Eclipse框架中实现了此预测模型，该框架以像素化热图的形式预测和可视化网页的复杂性。最后，我们通过对30个网页上104位用户的6240个评分进行了另一项用户研究，评估了该模型和工具预测。这项研究表明，我们的工具可以预测感知到的复杂性，并且与用户感知到的复杂性密切相关。