Gaze-based feedback in assessing media relevance

Cheng Ta Yang, Wen Sheng Chang, Fan Ning Cheng, Wei Guang Teng

Research output: Contribution to journalArticlepeer-review


To ease the problem of data overloading, it is crucial to understand the user behavior when s/he interacts with online contents, or more specifically, a web page containing several entries for further exploration. We devise to estimate the relevance of an entry to the user goal by observing eye movements as implicit feedback. Specifically, this study proposes a framework that assumes eye movement measures can be used to infer a user's cognition. A rating task was conducted in which subjects were required to judge whether an image was relevant to a word. Results showed that the total fixation duration and the fixation count can be used to discriminate between the relevant and irrelevant conditions; in contrast, the first fixation duration cannot. In addition, the subjective rating and relevancy manipulation interacted on the total fixation duration. Converging evidence verifies the assumption we have proposed.

Original languageEnglish
JournalJournal of Computers
Issue number2
Publication statusPublished - Jul 2011
Externally publishedYes


  • Eye movement
  • Relevance feedback
  • Social media

ASJC Scopus subject areas

  • Computer Science(all)


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