词眼注视在查询词预测中的作用
The Role of Word-Eye-Fixations for Query Term Prediction
摘要：
在整个搜索过程中，用户对所检查的SERP和网站的注视可以揭示他或她的搜索兴趣。凝视行为可以用眼动追踪来捕捉，并用词眼注视来描述。词眼注视包含用户在网页的每个单词上的累积注视持续时间。在这项工作中，我们分析了预测查询条件的字眼注视的作用。我们调查了一系列的会话功能，特别是凝视数据，与查询条件和训练模型预测查询条件之间的关系。我们使用的数据集的50个搜索会话通过实验室研究在社会科学领域获得。使用已建立的机器学习模型，即使只有很少的训练数据，我们也可以以极高的准确度预测查询词。特征分析表明，类别固定，查询相关性和会话主题包含我们的任务最有效的功能。
英文摘要：
Throughout the search process, the user's gaze on inspected SERPs and websites can reveal his or her search interests. Gaze behavior can be captured with eye tracking and described with word-eye-fixations. Word-eye-fixations contain the user's accumulated gaze fixation duration on each individual word of a web page. In this work, we analyze the role of word-eye-fixations for predicting query terms. We investigate the relationship between a range of in-session features, in particular, gaze data, with the query terms and train models for predicting query terms. We use a dataset of 50 search sessions obtained through a lab study in the social sciences domain. Using established machine learning models, we can predict query terms with comparably high accuracy, even with only little training data. Feature analysis shows that the categories Fixation, Query Relevance and Session Topic contain the most effective features for our task.
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。
