预测眼动跟踪辅助网页分割

IF 3 4区 计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS
Abdullah Sulayfani, Sukru Eraslan, Yeliz Yesilada
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引用次数: 0

摘要

为了提高网页的可访问性、衡量网页的视觉质量和美感等不同目的,人们提出了不同类型的算法来识别网页的视觉元素。其中一类算法通过分析源代码和网页的视觉表现来识别元素,而另一类算法则通过分析用户的眼球运动来发现有吸引力的元素。前一种方法建议将这两种方法结合起来,同时考虑网页的源代码和视觉表现以及用户在这些网页上的眼球运动。该方法的结果可视为眼动跟踪辅助网页分割。然而,由于眼动跟踪数据收集过程繁琐、耗时且昂贵,而且为每个网页收集眼动跟踪数据并不可行,因此我们的目标是开发一种无需眼动跟踪数据即可预测网页分割的模型。在本文中,我们使用不同的机器学习和深度学习算法进行了实验,结果表明 K-Nearest Neighbour (KNN) 模型的预测效果最好。我们提出了一个 KNN 模型,该模型可预测眼动跟踪辅助网页分割,F1 分数高达 78.74%。这项工作展示了机器学习算法如何在眼动跟踪数据的驱动下自动进行网页分割。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Predicting eye-tracking assisted web page segmentation

Predicting eye-tracking assisted web page segmentation

Different kinds of algorithms have been proposed to identify the visual elements of web pages for different purposes, such as improving web accessibility, measuring web page visual quality and aesthetics etc. One group of these algorithms identifies the elements by analyzing the source code and visual representation of web pages, whereas another group discovers the attractive elements by analyzing the eye movements of users. A previous approach proposes combining these two approaches to consider both the source code and visual representation of web pages and users’ eye movements on those pages. The result of the proposed approach can be considered eye-tracking-assisted web page segmentation. However, since the eye-tracking data collection procedure is elaborate, time-consuming, and expensive, and it is not feasible to collect eye-tracking data for each page, we aim to develop a model to predict such segmentation without requiring eye-tracking data. In this paper, we present our experiments with different Machine and Deep Learning algorithms and show that the K-Nearest Neighbour (KNN) model yields the best results in prediction. We present a KNN model that predicts eye-tracking-assisted web page segmentation with an F1-score of 78.74%. This work shows how an Machine Learning algorithm can automate web page segmentation driven by eye-tracking data.

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来源期刊
Multimedia Tools and Applications
Multimedia Tools and Applications 工程技术-工程:电子与电气
CiteScore
7.20
自引率
16.70%
发文量
2439
审稿时长
9.2 months
期刊介绍: Multimedia Tools and Applications publishes original research articles on multimedia development and system support tools as well as case studies of multimedia applications. It also features experimental and survey articles. The journal is intended for academics, practitioners, scientists and engineers who are involved in multimedia system research, design and applications. All papers are peer reviewed. Specific areas of interest include: - Multimedia Tools: - Multimedia Applications: - Prototype multimedia systems and platforms
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