Earticle

초록

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.

목차

Abstract
I. INTRODUCTION
II. METAVERSE FASHION ITEM DATASET
A. H&M Personalized Fashion Recommendations
B. Real-world metaverse 3D fashion item dataset
III. EXPERIMENTS
IV. CONCLUSION
ACKNOWLEDGMENT (Heading 5)
REFERENCES

키워드

저자

• SaeBom Lee [ Dept of Computer Engineering, Gachon University Seongnam-si, Republic of Korea ]
• Pankoo Kim [ Dept of Computer Engineering, Chosun University Gwangju, Republic of Korea ]
• Chang Choi [ Dept of Computer Engineering, Gachon University Seongnam-si, Republic of Korea ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국차세대컴퓨팅학회 학술대회

• 간기

  반년간

• 수록기간

  2021~2025

• 십진분류

  KDC 566 DDC 004