An Eye-Tracking Study of User Behavior in Web Image Search

2014 ◽  
pp. 170-182 ◽  
Author(s):  
Wanxuan Lu ◽  
Yunde Jia
Keyword(s):  
Eye Tracking ◽  
User Behavior ◽  
Image Search

Impact of bookshelf locations using eye-tracking methodology

2020 ◽  
Vol 41 (8/9) ◽  
pp. 617-629
Author(s):  
Sho Sato ◽  
Yukari Eto ◽  
Kotomi Iwaki ◽  
Tadashi Oyanagi ◽  
Yu Yasuma
Keyword(s):  
Eye Tracking ◽  
User Behavior ◽  
Public Library ◽  
Gaze Behavior ◽  
Content Type ◽  
User Behaviors ◽  
Significant Relationships ◽  
Vertical Location ◽  
The Right ◽  
The Impact

PurposeThis study aimed to understand better the user gaze behavior on bookshelves using eye-tracking technology.Design/methodology/approachAn eye-tracking experiment in a public library with 11 participants was performed. The impact of vertical shelf location of books on the number of times the books are looked at, the impact of horizontal location and the relationship between user behavior and location impact were examined by the findings.FindingsThe results showed that the vertical location of books has a significant impact on the number of times the books are looked at. More than 80% of the time spent looking at bookshelves was spent on books on the top to fourth rows. It was also revealed that the horizontal location of books has a little impact. Books located on the left side of shelves will be looked at significantly more often than those on the right side. No significant relationships between type of user behaviors and location impact were observed.Originality/valueThe study explored the impact of the vertical location of books on time spent looking at bookshelves using eye-tracking methodology. Few published studies do such experiments to address user gaze behavior on bookshelves. The study explored that the vertical location of books has a great impact, and horizontal location has a little impact on user gaze behavior.

scholarly journals Implicit relevance feedback from electroencephalography and eye tracking in image search

2018 ◽  
Vol 15 (2) ◽  
pp. 026002 ◽  
Author(s):  
Jan-Eike Golenia ◽  
Markus A Wenzel ◽  
Mihail Bogojeski ◽  
Benjamin Blankertz
Keyword(s):  
Eye Tracking ◽  
Relevance Feedback ◽  
Image Search

scholarly journals Gaze and Event Tracking for Evaluation of Recommendation-Driven Purchase

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1381 ◽  
Author(s):  
Piotr Sulikowski ◽  
Tomasz Zdziebko ◽  
Kristof Coussement ◽  
Krzysztof Dyczkowski ◽  
Krzysztof Kluza ◽  
...  
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Eye Tracking ◽  
User Behavior ◽  
Implicit Feedback ◽  
Physical Parameters ◽  
Object Model ◽  
Machine Learning Methods ◽  
Event Tracking ◽  
Item Presentation

Recommendation systems play an important role in e-commerce turnover by presenting personalized recommendations. Due to the vast amount of marketing content online, users are less susceptible to these suggestions. In addition to the accuracy of a recommendation, its presentation, layout, and other visual aspects can improve its effectiveness. This study evaluates the visual aspects of recommender interfaces. Vertical and horizontal recommendation layouts are tested, along with different visual intensity levels of item presentation, and conclusions obtained with a number of popular machine learning methods are discussed. Results from the implicit feedback study of the effectiveness of recommending interfaces for four major e-commerce websites are presented. Two different methods of observing user behavior were used, i.e., eye-tracking and document object model (DOM) implicit event tracking in the browser, which allowed collecting a large amount of data related to user activity and physical parameters of recommending interfaces. Results have been analyzed in order to compare the reliability and applicability of both methods. Observations made with eye tracking and event tracking led to similar results regarding recommendation interface evaluation. In general, vertical interfaces showed higher effectiveness compared to horizontal ones, with the first and second positions working best, and the worse performance of horizontal interfaces probably being connected with banner blindness. Neural networks provided the best modeling results of the recommendation-driven purchase (RDP) phenomenon.

Eye-tracking analysis of user behavior and performance in web search on large and small screens

2014 ◽  
Vol 66 (3) ◽  
pp. 526-544 ◽  
Author(s):  
Jaewon Kim ◽  
Paul Thomas ◽  
Ramesh Sankaranarayana ◽  
Tom Gedeon ◽  
Hwan-Jin Yoon
Keyword(s):  
Eye Tracking ◽  
Web Search ◽  
User Behavior ◽  
And Performance
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