Publication
Personalised image emotion-based recommender system
| datacite.subject.fos | Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática | pt_PT |
| dc.contributor.advisor | Barros, Márcia Cristina Afonso | |
| dc.contributor.advisor | Pires, Soraia Vanessa Meneses Alarcão Castelo de Almeida | |
| dc.contributor.author | Marcelino, Inês Guerreiro | |
| dc.date.accessioned | 2025-05-28T15:44:21Z | |
| dc.date.available | 2025-05-28T15:44:21Z | |
| dc.date.issued | 2025 | |
| dc.date.submitted | 2024 | |
| dc.description | Tese de Mestrado, Ciência de Dados, 2025, Universidade de Lisboa, Faculdade de Ciências | pt_PT |
| dc.description.abstract | In today’s digital age, users face the challenge of information overload,making it difficult to ac cess personalised and relevant multimedia content. Recommendation systems play a crucial role in addressing this issue by analysing large datasets and providing tailored content based on user preferences and behaviours. Traditional approaches, such as content-based and collaborative fil tering, have been widely explored, but they often neglect the emotional context of users. Although emotion-based recommendation systems have been studied, there remains a gap in knowledge about successfully integrating demographic data with emotional insights to enhance personali sation. This thesis addresses the problem of information overload by developing two systems, within the EmoRecSys project, which aims to enhance user experience by integrating emotional information into the recommendation process. We propose two systems: an emotion-based rec ommendation system, which uses a collaborative filtering approach, and a demographic-based recommendation system that leverages users’ characteristics to group them according to similari ties between user profiles. Our evaluation showed that the emotion-based recommendation system achieved the best results using the Non-Negative Matrix Factorisation algorithm, with precision achieving scores of 0.972 and 0.433 for Top@1 and Top@5, respectively. We further evaluated it with users. To do so, we compared the emotion-based system developed with content-based and random recommendation systems. In the first recommendation (Top@1), the emotion-based recommendation system presented a precision of 0.850, while the content-based and random rec ommendations presented precision scores of 0.800 and 0.700, respectively. | pt_PT |
| dc.identifier.uri | http://hdl.handle.net/10400.5/101087 | |
| dc.language.iso | eng | pt_PT |
| dc.subject | Emoções | pt_PT |
| dc.subject | Imagens | pt_PT |
| dc.subject | Sistemas de recomendação | pt_PT |
| dc.subject | Filtragem colaborativa | pt_PT |
| dc.subject | Filtragem demográfica | pt_PT |
| dc.subject | Teses de mestrado - 2025 | pt_PT |
| dc.title | Personalised image emotion-based recommender system | pt_PT |
| dc.type | master thesis | |
| dspace.entity.type | Publication | |
| rcaap.rights | openAccess | pt_PT |
| rcaap.type | masterThesis | pt_PT |
| thesis.degree.name | Mestrado em Ciência de Dados | pt_PT |