作者: Jeff Huang , Ryen W. White , Susan Dumais
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摘要: Understanding how people interact with search engines is important in improving quality. Web typically analyze queries and clicked results, but these actions provide limited signals regarding interaction. Laboratory studies often use richer methods such as gaze tracking, this impractical at scale. In paper, we examine mouse cursor behavior on engine results pages (SERPs), including not only clicks also movements hovers over different page regions. We: (i) report an eye-tracking study showing that position closely related to eye gaze, especially SERPs; (ii) present a scalable approach capture movements, analysis of result examination evident large-scale data; (iii) describe two applications (estimating relevance distinguishing good from bad abandonment) demonstrate the value capturing data. Our findings help us better understand searchers cursors SERPs can design more effective systems. tracking method may be useful non-search settings.