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Inferring User Preference in Good Abandonment from Eye Movements

  • Wanxuan Lu
  • Yunde JiaEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9098)

Abstract

Many studies have been done to investigate good abandonment, but only a few have utilized it to improve search engine performance. In this paper, we aim at inferring user preference in good abandonment. Particularly, we use eye movement data to infer which search result has satisfied user’s information need in each good abandonment instance. An eye-tracking experiment was conducted to capture user’s eye movement data in good abandonment search tasks. These data were transformed into histograms and sequences on which we applied popular machine learning algorithms for the inference. Our results show that the approach can infer user preference with reasonable accuracy.

Keywords

Good abandonment User preference inference Eye movement Search result preference Eye-tracking 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Beijing Laboratory of Intelligent Information Technology, School of Computer ScienceBeijing Institute of TechnologyBeijingChina

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