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Publicação
Emotion-based Image Recommendation System
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Resumo(s)
Recommendation systems play a crucial role in today’s digital society by helping users make decisions
when so much information is available. Among other types of content, images are ingrained in people’s daily lives and can significantly influence their emotional states. Because of this intense emotional
impact, images have become important tools in therapeutic contexts for the treatment of dementia and
post-traumatic stress. By considering emotional responses, an image recommendation system can personalize and make recommendations according to emotions, increasing relevance and effectiveness. This
work focuses on developing an image recommendation system where emotions felt by the user are a feature for the recommendation. This study was done with a new dataset of the EmoRecSys Project that
is still under construction. To do that, we developed an emotion-based recommendation system based
on the user’s declared explicit emotions from the dataset. To evaluate the performance of the proposed
recommendation system, two more recommendation systems that are more widely studied in the literature were developed: pixel and metadata, as well as a random recommendation system that works as
a baseline. The systems’ performance was evaluated using precision, recall, f1-score, and normalized
discounted cumulative gain. The results at top@10 for precision were 0.119, 0.113, and 0.111 for the
emotion-, pixel-, and metadata-based, respectively. Regarding recall, at top@10, the recalls are 0.542,
0.505, and 0.492 for the emotion-, pixel-, and metadata-based, respectively. In the normalized discounted
cumulative gain, emotions, pixels, and annotations have top@10 of 0.302, 0.299, and 0.285. The results
show that the recommendation of images based on emotions achieve better results than using state of the
art features, such as pixel and metadata similarities
Descrição
Tese de mestrado, Bioinformática e Biologia Computacional, 2025, Universidade de Lisboa, Faculdade de Ciências
Palavras-chave
Filtragem baseada em Conteúdo Sistemas de Recomendação Emoções Imagens Teses de mestrado - 2025