earticle

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.