earticle

영어

As the metaverse evolves into a dynamic environment where users express their identity through avatars and fashion items, developing effective recommendation systems based on user interactions remains a significant challenge. To address this, we propose a novel technology that leverages Multi-Layer Perceptron (MLP)-based RGB and density values, processed using a Volume Rendering technique to convert them into a single-pixel representation. This approach enhances the accuracy of personalized fashion item recommendations by capturing visual and interactive data more precisely. Our model was trained on the publicly available H&M Personalized Fashion Recommendations dataset, achieving 79% similarity by measuring cosine similarity between item vectors. Additionally, we evaluated the system using data provided by a company that creates fashion items for real metaverse environments. Item IDs were used to define the source and target URLs, and the similarity between the items was measured to determine recommendations. This evaluation confirmed the model’s effectiveness in real-world scenarios.