arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2001.05152cs.HCcs.CVcs.IR

Relevance Prediction from Eye-movements Using Semi-interpretable Convolutional Neural Networks

The University of Texas at Austin(德克萨斯大学奥斯汀分校);Lockheed Martin Corporation(洛克希德·马丁公司)

Nilavra Bhattacharya, Somnath Rakshit, Jacek Gwizdka, Paul Kogut

英文摘要:

We propose an image-classification method to predict the perceived-relevance of text documents from eye-movements. An eye-tracking study was conducted where participants read short news articles, and rated them as relevant or irrelevant for answering a trigger question. We encode participants' eye-movement scanpaths as images, and then train a convolutional neural network classifier using these scanpath images. The trained classifier is used to predict participants' perceived-relevance of news articles from the corresponding scanpath images. This method is content-independent, as the classifier does not require knowledge of the screen-content, or the user's information-task. Even with little data, the image classifier can predict perceived-relevance with up to 80% accuracy. When compared to similar eye-tracking studies from the literature, this scanpath image classification method outperforms previously reported metrics by appreciable margins. We also attempt to interpret how the image classifier differentiates between scanpaths on relevant and irrelevant documents.

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。

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