Earticle

초록

영어

The increasing demand for halal cosmetic products, especially in Muslim countries, has shown significant challenges for consumers seeking products that follow Islamic principles. Although various studies attempt to recommend halal status, mainly considering the discrete and specific relations within individual cosmetics, they ignore the high-order and complex relations between cosmetics and ingredients. To solve it, we propose a halal cosmetic recommendation framework that leverages a knowledge graph of cosmetics and their ingredients to recommend similar cosmetics and halal cosmetic predictions. Specifically, we construct a cosmetic knowledge graph representing the relations between various cosmetics, ingredients, and their properties. We then propose a pre-trained relational graph attention network model with residual connections identity mapping to learn the structural relation between entities in the knowledge graph. The pretrained model is then employed on downstream cosmetic data to recommend similar cosmetics and predict halal standards.

목차

Abstract
I. INTRODUCTION
II. METHODOLOGY
A. Cosmetic Knowledge Graph Construction
B. Model Architecture
C. Pre-training and fine-tuning
D. Inference
III. CONCLUSION AND FUTURE WORK
ACKNOWLEDGMENT
REFERENCES

키워드

저자

• Van Thuy Hoang [ Department of Artificial Intelligence The Catholic University of Korea Bucheon, Republic of Korea ]
• Ho-Chan Yang [ Department of Computer Science & Information Engineering The Catholic University of Korea Bucheon, Republic of Korea ]
• Ju-Hee Shim [ Department of Artificial Intelligence The Catholic University of Korea Bucheon, Republic of Korea ]
• O-Joun Lee [ Department of Artificial Intelligence The Catholic University of Korea Bucheon, Republic of Korea ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국차세대컴퓨팅학회 학술대회

• 간기

  반년간

• 수록기간

  2021~2025

• 십진분류

  KDC 566 DDC 004