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• W2100641569 abstract "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." @default.
• W2100641569 created "2016-06-24" @default.
• W2100641569 creator A5038724310 @default.
• W2100641569 creator A5060506151 @default.
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• W2100641569 creator A5074972269 @default.
• W2100641569 date "2011-07-01" @default.
• W2100641569 modified "2023-09-25" @default.
• W2100641569 title "Assessing Media Relevance via Eye Tracking" @default.
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• W2100641569 doi "https://doi.org/10.1109/asonam.2011.122" @default.
• W2100641569 hasPublicationYear "2011" @default.
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