Improving the understanding of web user behaviors through machine learning analysis of eye-tracking data
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Metadatos
Mostrar el registro completo del ítemcomunitat-uji-handle:10234/9
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comunitat-uji-handle3:10234/8636
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Título
Improving the understanding of web user behaviors through machine learning analysis of eye-tracking dataAutoría
Fecha de publicación
2023-07Editor
SpringerISSN
0924-1868; 1573-1391Cita bibliográfica
Castilla, D., Del Tejo Catalá, O., Pons, P. et al. Improving the understanding of web user behaviors through machine learning analysis of eye-tracking data. User Model User-Adap Inter (2023). https://doi.org/10.1007/s11257-023-09373-yTipo de documento
info:eu-repo/semantics/articleVersión de la editorial
https://link.springer.com/article/10.1007/s11257-023-09373-yVersión
info:eu-repo/semantics/publishedVersionPalabras clave / Materias
Resumen
Eye-tracking techniques are widely used to analyze user behavior. While eye-trackers collect valuable quantitative data, the results are often described in a qualitative manner due to the lack of a model that interprets ... [+]
Eye-tracking techniques are widely used to analyze user behavior. While eye-trackers collect valuable quantitative data, the results are often described in a qualitative manner due to the lack of a model that interprets the gaze trajectories generated by routine tasks, such as reading or comparing two products. The aim of this work is to propose a new quantitative way to analyze gaze trajectories (scanpaths) using machine learning. We conducted a within-subjects study (N = 30) testing six different tasks that simulated specific user behaviors in web sites (attentional, comparing two images, reading in different contexts, and free surfing). We evaluated the scanpath results with three different classifiers (long short-term memory recurrent neural network—LSTM, random forest, and multilayer perceptron neural network—MLP) to discriminate between tasks. The results revealed that it is possible to classify and distinguish between the 6 different web behaviors proposed in this study based on the user’s scanpath. The classifier that achieved the best results was the LSTM, with a 95.7% accuracy. To the best of our knowledge, this is the first study to provide insight about MLP and LSTM classifiers to discriminate between tasks. In the discussion, we propose practical implications of the study results. [-]
Publicado en
User Modeling and User-Adapted Interaction, (2023)Entidad financiadora
CRUE-CSIC | IVACE
Código del proyecto o subvención
IMAMCN/2021/1
Derechos de acceso
info:eu-repo/semantics/openAccess
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- PSB_Articles [1315]