"The Best of Both Worlds!": Integration of Web Page and Eye Tracking Data Driven Approaches for Automatic AOI Detection

Date

2020-02-01

Author

Eraslan, Sukru
Yesilada, Yeliz
Harper, Simon

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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Web pages are composed of different kinds of elements (menus, adverts, etc.). Segmenting pages into their elements has long been important in understanding how people experience those pages and in making those experiences "better." Many approaches have been proposed that relate the resultant elements with the underlying source code; however, they do not consider users' interactions. Another group of approaches analyses eye movements of users to discover areas that interest or attract them (i.e., areas of interest or AOIs). Although these approaches consider how users interact with web pages, they do not relate AOIs with the underlying source code. We propose a novel approach that integrates web page and eye tracking data driven approaches for automatic AOI detection. This approach segments an entire web page into its AOIs by considering users' interactions and relates AOIs with the underlying source code. Based on the Adjusted Rand Index measure, our approach provides the most similar segmentation to the ground-truth segmentation compared to its individual components.

Subject Keywords

Computer Networks and Communications

URI

https://hdl.handle.net/11511/67095

Journal

ACM TRANSACTIONS ON THE WEB

DOI

https://doi.org/10.1145/3372497

Collections

Engineering, Article

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Citation Formats

S. Eraslan, Y. Yesilada, and S. Harper, ““The Best of Both Worlds!”: Integration of Web Page and Eye Tracking Data Driven Approaches for Automatic AOI Detection,” ACM TRANSACTIONS ON THE WEB, pp. 0–0, 2020, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/67095.