Reference : The Elements of Visual Art Recommendation: Learning Latent Semantic Representations o...

	Document type :	Scientific congresses, symposiums and conference proceedings : Paper published in a book
	Discipline(s) :	Engineering, computing & technology : Computer science
	Focus Areas :	Computational Sciences
	To cite this reference:	http://hdl.handle.net/10993/54495

Title :	The Elements of Visual Art Recommendation: Learning Latent Semantic Representations of Paintings
Language :	English
Author, co-author :	Yilma, Bereket Abera [University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Computer Science (DCS) >]
	Leiva, Luis A. [University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Computer Science (DCS) >]
Publication date :	Apr-2023
Main document title :	Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23)
Peer reviewed :	Yes
Audience :	International
Event name :	Conference on Human Factors in Computing Systems (CHI ’23)
Event date :	22-04-2023
Keywords :	[en] Recommendation systems ; Personalization ; Machine Learning
Abstract :	[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.
Target :	Researchers ; Professionals ; Students ; General public ; Others
Permalink :	http://hdl.handle.net/10993/54495
DOI :	10.1145/3544548.3581477
Framework Programme / European Project :	H2020 ; 101071147 - SYMBIOTIK - Context-aware adaptive visualizations for critical decision making
FnR project :	FnR ; FNR15722813 > Luis Leiva > BANANA > Brainsourcing For Affective Attention Estimation > 01/10/2021 > 30/09/2024 > 2021

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