论文标题
基于凝视数据分析的短跨度窗口上网络用户视觉关注的连续预测
Continuous Prediction of Web User Visual Attention on short span Windows based on Gaze Data Analytics
论文作者
论文摘要
识别网页的个性化显着区域的现有方法，没有考虑网络用户注视或其内容的更改的动态行为。因此，本文提出了访问意图的概念，这是网络用户在一定时间段内的视觉关注的指标，在不同感兴趣的领域，短时间窗口窗口很短。该指标提供了有关用户将在一个时间窗口中访问的网站感兴趣领域的信息，而无需知道每个窗口中网站的结构。我们的方法利用人口级的一般目光模式和用户的视觉动力学。我们在实验上表明，可以使用少量用户通过多标签分类模型来进行这样的预测，从而获得曲线下的平均面积为84.3％，平均准确度为79％，单个兴趣面积的准确度为77％。此外，在每组交叉验证评估中都一致选择用户的视觉动力学功能。
The existing approaches to identify personalized salience zones of a Web page do not consider the dynamic behavior in time of the Web user's gaze or the alterations of its content. For this reason, this paper proposes the concept of visit intention, an indicator of the visual attention of a Web user in a certain period of time, short span time windows, in different areas of interest. This indicator gives information on the areas of interest of a website that will be visited by a user over a time window, without requiring to know the structure of the site in each window. Our approach leverages the population-level general gaze patterns and the user's visual kinetics. We show experimentally that it is possible to conduct such a prediction through multilabel classification models using a small number of users, obtaining an average area under curve of 84.3 %, an average accuracy of 79 %, and an individual area of interest accuracy of 77 %. Furthermore, the user's visual kinetics features are consistently selected in every set of a cross-validation evaluation.
