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Article

Gaze and Event Tracking for Evaluation of Recommendation-Driven Purchase

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Faculty of Information Technology and Computer Science, West Pomeranian University of Technology, ul. Żołnierska 49, 71-210 Szczecin, Poland
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Faculty of Economics, Finance and Management, University of Szczecin, ul. Adama Mickiewicza 64, 71-101 Szczecin, Poland
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IÉSEG School of Management, 3 Rue de la Digue, F-59000 Lille, France
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LEM-CNRS 9221, 3 Rue de la Digue, F-59000 Lille, France
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Faculty of Mathematics and Computer Science, Adam Mickiewicz University, ul. Uniwersytetu Poznańskiego 4, 61-614 Poznań, Poland
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Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering, AGH University of Science and Technology, al. Adama Mickiewicza 30, 30-059 Kraków, Poland
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Faculty of Business and Management, Wrocław University of Economics and Business, ul. Komandorska 118/120, 53-345 Wrocław, Poland
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Author to whom correspondence should be addressed.
Academic Editor: Jungpil Shin
Sensors 2021, 21(4), 1381; https://doi.org/10.3390/s21041381
Received: 13 January 2021 / Revised: 4 February 2021 / Accepted: 11 February 2021 / Published: 16 February 2021
(This article belongs to the Special Issue Vision and Sensor-Based Sensing in Human Action Recognition)
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. View Full-Text
Keywords: e-commerce; human activity recognition; human–computer interaction; eye tracking; event tracking; mouse tracking; recommender systems; visual; layout; implicit feedback e-commerce; human activity recognition; human–computer interaction; eye tracking; event tracking; mouse tracking; recommender systems; visual; layout; implicit feedback
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MDPI and ACS Style

Sulikowski, P.; Zdziebko, T.; Coussement, K.; Dyczkowski, K.; Kluza, K.; Sachpazidu-Wójcicka, K. Gaze and Event Tracking for Evaluation of Recommendation-Driven Purchase. Sensors 2021, 21, 1381. https://doi.org/10.3390/s21041381

AMA Style

Sulikowski P, Zdziebko T, Coussement K, Dyczkowski K, Kluza K, Sachpazidu-Wójcicka K. Gaze and Event Tracking for Evaluation of Recommendation-Driven Purchase. Sensors. 2021; 21(4):1381. https://doi.org/10.3390/s21041381

Chicago/Turabian Style

Sulikowski, Piotr, Tomasz Zdziebko, Kristof Coussement, Krzysztof Dyczkowski, Krzysztof Kluza, and Karina Sachpazidu-Wójcicka. 2021. "Gaze and Event Tracking for Evaluation of Recommendation-Driven Purchase" Sensors 21, no. 4: 1381. https://doi.org/10.3390/s21041381

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