The Elements of Visual Art Recommendation: Learning Latent Semantic Representations of Paintings

YILMA, Bereket Abera; LEIVA, Luis A.

doi:10.1145/3544548.3581477

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The Elements of Visual Art Recommendation: Learning Latent Semantic Representations of Paintings

YILMA, Bereket Abera; LEIVA, Luis A.

2023 • In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23)

Peer reviewed

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https://hdl.handle.net/10993/54495

DOI
10.1145/3544548.3581477

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Keywords :

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.

Disciplines :

Computer science

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)

External co-authors :

yes

Language :

English

Title :

The Elements of Visual Art Recommendation: Learning Latent Semantic Representations of Paintings

Publication date :

April 2023

Event name :

Conference on Human Factors in Computing Systems (CHI ’23)

Event date :

22-04-2023

Audience :

International

Main work title :

Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23)

Peer reviewed :

Peer reviewed

Focus Area :

Computational Sciences

European Projects :

HE - 101071147 - SYMBIOTIK - Context-aware adaptive visualizations for critical decision making

FnR Project :

FNR15722813 - Brainsourcing For Affective Attention Estimation, 2021 (01/02/2022-31/01/2025) - Luis Leiva

Funders :

CE - Commission Européenne

Available on ORBilu :

since 01 March 2023

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