Cloud-enabled style-aware artwork composite recommendation based on correlation graph

Recommending visually coherent and stylistically diverse sets of artworks is a challenging task in digital curation, interior design, and personalized visual content services. Unlike traditional recommendation problems that focus on individual item relevance, composite artwork recommendation require...

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Vydáno v:Journal of cloud computing : advances, systems and applications Ročník 14; číslo 1; s. 68 - 13
Hlavní autoři: Guo, Hongyuan, Anari, Maryam Saberi, Rezaee, Khosro
Médium: Journal Article
Jazyk:angličtina
Vydáno: Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2025
Springer Nature B.V
SpringerOpen
Témata:
Aesthetics
Algorithms
Art works
Artwork style
Cloud computing
Collaborative Intelligence in Cloud – Synergy Between Large and Small Models
Compatibility
Composite recommendation
Computer Communication Networks
Computer Science
Computer System Implementation
Computer Systems Organization and Communication Networks
Correlation graph
Fashion models
Graph theory
Information Systems Applications (incl.Internet)
Interior design
Metadata
Neural networks
Parallel processing
Performance evaluation
Recommender systems
Search algorithms
Semantics
Software Engineering/Programming and Operating Systems
Special Purpose and Application-Based Systems
Composite recommendation
Cloud computing
Artwork style
Compatibility
Correlation graph
ISSN:2192-113X, 2192-113X
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Popis
Shrnutí:Recommending visually coherent and stylistically diverse sets of artworks is a challenging task in digital curation, interior design, and personalized visual content services. Unlike traditional recommendation problems that focus on individual item relevance, composite artwork recommendation requires selecting a group of items that together satisfy a user’s stylistic intent while maintaining aesthetic compatibility. In this paper, we introduce a novel cloud-enabled graph-based framework for style-aware artwork composite recommendation. We construct an artwork correlation graph that models both the stylistic descriptors of individual artworks and their empirical compatibility based on historical co-occurrence. By leveraging distributed computation in cloud environments, our framework efficiently handles large-scale artwork collections and accelerates graph search. Given a user-defined set of style tags, our method identifies a minimal and connected subset of artworks that collectively cover the desired styles and form a coherent set in the graph. We formalize this task as a constrained subgraph selection problem and propose an efficient graph search algorithm supported by cloud-based parallelization to solve it. Experimental results on real-world artwork datasets demonstrate that our approach significantly outperforms existing baselines in terms of style coverage, visual harmony, and recommendation compactness. This work offers a principled and scalable cloud-oriented solution for generating aesthetically balanced and contextually appropriate artwork combinations.
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ISSN:2192-113X
2192-113X
DOI:10.1186/s13677-025-00800-6