Predicting Trending Elements on Web Pages Using Eye-Tracking Data

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Thesis Naziha Shekh Khalil.pdf

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2022-8-31

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Shekh Khalil , Naziha

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Eye-tracking data can be used to understand how users interact with web pages and such understanding can be facilitated for different purposes, for example, it can be used to support better accessibility for disabled users or better usability for all users. However, understanding the sequential behavior of a group of users is challenging from eye-tracking data because individual eye movement sequences, i.e. scanpaths, tend to be complicated and different from each other. Previous work proposes an algorithm called Scanpath Trend Analysis (STA), which brings multiple individual eye movement sequences together and identifies a single representative sequence as a trending path. However, to determine such a path on a web page, an eye-tracking experiment on that page is required. We aim to investigate whether we could identify a trending path on a web page without a new eye-tracking dataset. This thesis shows the experiments towards predicting trending elements without an eye-tracking dataset using different machine learning classifiers based on web page features. We used two pre-collected eye-tracking datasets from previous research to validate the experiments. The results demonstrate that the k-nearest neighbors (KNN) classifier can achieve, from the first dataset, an average F1 score of 0.91 for the browsing task, and an average F1 score of 0.88 for the searching task. With the second dataset, similar results are acquired for the browsing task, however, the synthesis task was not as successful as the browsing task results. Overall, the results show the possibility of predicting the trending elements without using eye-tracking data.

Subject Keywords

Eye-tracking, Trending path, Scanpath trend analysis, Area of interest, Machine learning, Göz izleme, Trend yol, Scanpath trend analysis, Makine öğrenme

URI

https://hdl.handle.net/11511/101126

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Northern Cyprus Campus, Thesis

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Citation Formats

N. Shekh Khalil, “Predicting Trending Elements on Web Pages Using Eye-Tracking Data,” M.S. - Master of Science, Middle East Technical University, 2022.