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Personalised image emotion-based recommender system
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Resumo(s)
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.
Descrição
Tese de Mestrado, Ciência de Dados, 2025, Universidade de Lisboa, Faculdade de Ciências
Palavras-chave
Emoções Imagens Sistemas de recomendação Filtragem colaborativa Filtragem demográfica Teses de mestrado - 2025