QoE-driven cloud intelligence for personalized visual art recommendation

With the rapid development of cloud-based creative platforms, personalized visual art recommendation has attracted increasing attention in recent years. Online illustration services usually host a large number of artworks with diverse styles and themes, making it difficult for users to efficiently discover content that matches their visual preferences. Existing recommendation methods mainly emphasize preference matching at the individual artwork level, while the overall quality of experience (QoE) perceived from a recommended artwork set is often insufficiently considered. In this paper, we propose a QoE-driven cloud intelligence approach for personalized visual art recommendation. Based on historical user interaction data, a weighted artwork correlation graph is constructed to characterize perceptual relationships among artworks. By jointly considering user intent coverage and cross-artwork perceptual consistency, a QoE-aware optimization strategy is designed to generate a compact and coherent recommendation list. Experimental results on a large-scale dataset show that the proposed approach achieves better performance than several baseline methods in terms of recommendation accuracy and QoE-related metrics, while maintaining reasonable computational efficiency in cloud environments. These results indicate that incorporating QoE considerations into cloud-based visual art recommendation can improve user experience and service effectiveness.