Reference : The Elements of Visual Art Recommendation: Learning Latent Semantic Representations o...
Scientific congresses, symposiums and conference proceedings : Paper published in a book
Engineering, computing & technology : Computer science
Computational Sciences
http://hdl.handle.net/10993/54495
The Elements of Visual Art Recommendation: Learning Latent Semantic Representations of Paintings
English
Yilma, Bereket Abera mailto [University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Computer Science (DCS) >]
Leiva, Luis A. mailto [University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Computer Science (DCS) >]
Apr-2023
Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23)
Yes
International
Conference on Human Factors in Computing Systems (CHI ’23)
22-04-2023
[en] Recommendation systems ; Personalization ; Machine Learning
[en] Artwork recommendation is challenging because it requires understanding how users interact with highly subjective content, the complexity of the concepts embedded within the artwork, and the emotional and cognitive reflections they may trigger in users. In this paper, we focus on efficiently capturing the elements (i.e., latent semantic relationships) of visual art for personalized recommendation. We propose and study recommender systems based on textual and visual feature learning techniques, as well as their combinations. We then perform a small-scale and a large-scale user-centric evaluation of the quality of the recommendations.
Our results indicate that textual features compare favourably with visual ones, whereas a fusion of both captures the most suitable hidden semantic relationships for artwork recommendation. Ultimately, this paper contributes to our understanding of how to deliver content that suitably matches the user's interests and how they are perceived.
Researchers ; Professionals ; Students ; General public ; Others
http://hdl.handle.net/10993/54495
10.1145/3544548.3581477
H2020 ; 101071147 - SYMBIOTIK - Context-aware adaptive visualizations for critical decision making
FnR ; FNR15722813 > Luis Leiva > BANANA > Brainsourcing For Affective Attention Estimation > 01/10/2021 > 30/09/2024 > 2021

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