Predicting Trending Elements on Web Pages Using Machine Learning

AbstractEye-tracking data can be used to understand how users interact with web pages. Understanding the eye-movement sequences of multiple users is a challenging task because the sequence followed by each user tends to be different. Scanpath Trend Analysis (STA) brings multiple individual eye-movement sequences together and identifies a representative sequence as a trending path. However, eye-tracking data on a web page is required to determine the trending path. Our aim here is to investigate whether we can train Machine Learning (ML) algorithms to identify trending elements on a web page without collecting eye-tracking data on that web page. This article presents our experiments with different ML classification algorithms towards achieving that goal. To validate the experiments, we used two datasets from previous research, the first one included browsing and searching tasks and the second one included browsing and synthesis tasks. Our experiments show that the k-nearest neighbors algorithm (KNN) model can successfully identify the trending elements in the first dataset for both browsing (F1=≈91%) and searching tasks (F1=≈88%). However, the second dataset's synthesis task results were not as successful as its browsing task results. Our work here shows that a model can be created to predict the trending elements in web pages solely with web page features but the task is a critical factor in the success of prediction.Keywords: Eye-trackingtrending pathscanpath trend analysismachine learning Disclosure statementNo potential conflict of interest was reported by the author(s).Notes1 Throughout this paper, we use AOI/segments interchangeably with visual elements of web pages.2 https://developers.google.com/web/tools/puppeteerAdditional informationNotes on contributorsNaziha Shekh KhalilNaziha Shekh Khalil holds BSc and MSc degrees in Computer Engineering from Middle East Technical University Northern Cyprus. Her research focuses on enhancing user experiences in the digital world through human-computer systems.Sukru EraslanSukru Eraslan is a lecturer at Middle East Technical University Northern Cyprus Campus. He completed his PhD in Computer Science at the University of Manchester. He then worked as a research associate at METU NCC and the University of Manchester. His primary research interests are centred around human–computer interaction. https://users.metu.edu.tr/seraslan/index.htmlYeliz YesiladaYeliz Yesilada earned her PhD in Computer Science from the University of Manchester in the UK. She is a Professor at Middle East Technical University Northern Cyprus Campus. Her primary research interest is the human–computer interaction, particularly user behaviour analysis and modelling, the mobile Web, and eye-tracking. https://www.yelizyesilada.info.