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 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 ...