Geodesic
Machine Learning Based Approach for Exploring Online Shopping Behavior and Preferences with Eye Tracking
Computer Science and Information Systems, Tome 21 (2024) no. 2.

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In light of advancements in information technology and the widespread impact of the COVID-19 pandemic, consumer behavior has undergone a significant transformation, shifting from traditional in-store shopping to the realm of online retailing. This shift has notably accelerated the growth of the online retail sector. An essential advantage offered by e-commerce lies in its ability to accumulate and analyze user data, encompassing browsing and purchase histories, through its recommendation systems. Nevertheless, prevailing methodologies predominantly rely on historical user data, which often lack the dynamism required to comprehend immediate user responses and emotional states during online interactions. Recognizing the substantial influence of visual stimuli on human perception, this study leverages eye-tracking technology to investigate online consumer behavior. The research captures the visual engagement of 60 healthy participants while they engage in online shopping, while also taking note of their preferred items for purchase. Subsequently, we apply statistical analysis and machine learning models to unravel the impact of visual complexity, consumer considerations, and preferred items, thereby providing valuable insights for the design of e-commerce platforms. Our findings indicate that the integration of eye-tracking data into e-commerce recommendation systems is conducive to enhancing their performance. Furthermore, machine learning algorithms exhibited remarkable classification capabilities when combined with eye-tracking data. Notably, during the purchase of hedonic products, participants primarily fixated on product images, whereas for utilitarian products, equal attention was dedicated to images, prices, reviews, and sales volume. These insights hold significant potential to augment the effectiveness of e-commerce marketing endeavors.
Keywords: recommender systems, eye tracking, shopping preferences, machine learning, consideration factors 
@article{CSIS_2024_21_2_a10,
     author = {Zhenyao Liu and Wei-Chang Yeh and Ke-Yun Lin and Hota Chia-Sheng Lin and Chuan-Yu Chang},
     title = {Machine {Learning} {Based} {Approach} for {Exploring} {Online} {Shopping} {Behavior} and {Preferences} with {Eye} {Tracking}},
     journal = {Computer Science and Information Systems},
     publisher = {mathdoc},
     volume = {21},
     number = {2},
     year = {2024},
     url = {http://geodesic.mathdoc.fr/item/CSIS_2024_21_2_a10/}
}
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Zhenyao Liu; Wei-Chang Yeh; Ke-Yun Lin; Hota Chia-Sheng Lin; Chuan-Yu Chang. Machine Learning Based Approach for Exploring Online Shopping Behavior and Preferences with Eye Tracking. Computer Science and Information Systems, Tome 21 (2024) no. 2. http://geodesic.mathdoc.fr/item/CSIS_2024_21_2_a10/