Publicação
Sistema inteligente para análise de atenção visual baseado em eye tracking e inteligência artificial focado em UI/UX
| datacite.subject.fos | Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática | |
| datacite.subject.sdg | 09:Indústria, Inovação e Infraestruturas | |
| dc.contributor.advisor | Teixeira, Ana Rita Assunção | |
| dc.contributor.advisor | Marreiros, Maria Goreti Carvalho | |
| dc.contributor.author | GONÇALVES, DIOGO FERREIRA | |
| dc.date.accessioned | 2026-09-18T14:43:02Z | |
| dc.date.available | 2026-09-18T14:43:02Z | |
| dc.date.issued | 2026-07-23 | |
| dc.description.abstract | The rapid growth of e-commerce has intensified the need for personalised and engaging user experiences. Understanding users’ visual attention and emotional responses while interacting with digital interfaces is essential for designing adaptive, effective, and user-centered websites. However, current approaches often address visual attention, emotional analysis, or interface adaptation separately, lacking a unified intelligent system that integrates these components. A systematic literature review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology was conducted to identify existing research on attention metrics, interface design, emotion recognition, and Artificial Intelligence (AI)-driven adaptation. The review revealed a gap that this thesis explores, as few studies link gaze patterns to explicit user preference in a way that could enable adaptive interface personalisation. The proposed solution is built around two complementary eye-tracking experiments and a multimodal predictive pipeline. The first experiment investigated how spatial position and presentation style influence visual attention within structured product grids. The second, choice-based experiment required participants to select their preferred product from competing alternatives, producing a labelled dataset that combines behavioural, oculomotor, physiological, and contextual features. Five predictive models were trained and evaluated on the resulting dataset, Random Forest, Extreme Gradient Boost Classifier, Support Vector Machine, Multilayer Perceptron, and Extreme Gradient Boost Ranker. Among these, Random Forest achieved the highest overall performance, correctly identifying the selected product as the top-ranked item in approximately 78% of choice tasks, while the Extreme Gradient Boost Ranker also demonstrated strong ranking performance. These findings demonstrate that eye-tracking data, combined with machine learning, can accurately infer user preferences, providing a foundation for adaptive and personalised e-commerce interfaces. | eng |
| dc.description.sponsorship | Financiado | |
| dc.identifier.tid | 204362253 | |
| dc.identifier.uri | http://hdl.handle.net/10400.22/32748 | |
| dc.language.iso | eng | |
| dc.rights.uri | N/A | |
| dc.subject | Eye-tracking | |
| dc.subject | AI | |
| dc.subject | Machine Learning | |
| dc.subject | Visual attention | |
| dc.subject | Product Preference | |
| dc.subject | Rank ing | |
| dc.subject | UX/UI | |
| dc.subject | E-commerce | |
| dc.subject | Adaptive Interfaces | |
| dc.title | Sistema inteligente para análise de atenção visual baseado em eye tracking e inteligência artificial focado em UI/UX | por |
| dc.title.alternative | An intelligent system for visual attention analysis based on eye tracking and artificial intelligence focused on UI/UX | eng |
| dc.type | master thesis | |
| dspace.entity.type | Publication | |
| thesis.degree.name | Mestrado em Engenharia Informática |