Predicting Trending Elements on Web Pages Using Eye-Tracking Data

Download

Thesis Naziha Shekh Khalil.pdf

Date

2022-8-31

Author

Shekh Khalil , Naziha

Metadata

Show full item record

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

Item Usage Stats

328
views

162
downloads

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

Collections

Northern Cyprus Campus, Thesis

Suggestions

OpenMETU
Core

Eye Tracking Scanpath Analysis Techniques on Web Pages: A Survey, Evaluation and Comparison Eraslan, Sukru; Yesilada, Yeliz; Harper, Simon (2016-01-01) Eye tracking has commonly been used to investigate how users interact with web pages, with the goal of improving their usability. This article comprehensively revisits the techniques that could be applicable to eye tracking data for analysing user scanpaths on web pages. It also uses a third-party eye tracking study to compare these techniques. This allows researchers to recognise existing techniques for their goals, understand how they work and know their strengths and limitations so that they can make an ...
Trends in Eye Tracking Scanpaths: Segmentation Effect? Eraslan, Sukru; Yesilada, Yeliz; Harper, Simon (2016-07-13) Eye tracking has been widely used to investigate user interactions with the Web to improve user experience. In our previous work, we developed an algorithm called Scanpath Trend Analysis (STA) that analyses eye movement sequences (i.e., scanpaths) of multiple users on a web page and identifies their most commonly followed path in terms of the visual elements of the page. These visual elements are mainly the segments of a page generated by automated segmentation approaches. In our previous work, we also show...
Discovering Visual Elements of Web Pages and Their Roles: Users' Perception Akpinar, M. Elgin; Yesilada, Yeliz (2017-11-01) Web pages typically include many visual elements such as header and footer to support interaction with the user. However, if web pages do not comply with web accessibility guidelines, and these visual elements are not explicitly encoded in the underlying source code, they become inaccessible in alternative presentations, such as audio. This article presents an automatic role detection approach to identify visual elements in web pages and their roles. The system architecture has three major components: autom...
Motor Imgeleme EEG Verisi Toplama Halıcı, Uğur; Rezaei Tabar, Yousef(2015-12-31) Bir birinden ayırt edilebilen beyin sinyalleri kullanılarak bir harf kodlamasına gidilmesinin bu işlemi hızlandıracağını ve motor imgeleme EEG kayıtlarının bu amaç için uygun olduğunudüşünmekteyiz. 1 Doktora ve 1 Y. Lisans tez ogrencimin koordineli olarak yapacakları BCI konuşma sentezi tez çalışmaları için bu projede motor imgeleme EEG verisi toplamaya yönelik çalışmaları başlatmayı amaçlamaktayız.
Automatic identification and role detection of visual elements in web pages Akpınar, Mehmet Elgin; Karagöz, Pınar; Yeşilada, Yeliz; Department of Computer Engineering (2014) Web pages typically include many visual elements to support interaction with the user. However, if visual elements do not comply with web accessibility guidelines, they become inaccessible in alternative presentations, such as audio. This study presents an automatic role detection approach to identify visual elements in web pages and their roles. The system architecture has three major components: automatic identification of visual elements in web pages; automatic generation of heuristic rules from the know...

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.