Virtual Dressing Sense

Abstract

Clothing, an essential aspect of human life, has undergone significant changes due to technological advancements. Most people have shifted their shopping preferences from traditional brick-and-mortar stores to online platforms. However, this transition has introduced an interesting challenge in the clothing industry: providing accurate clothing recommendations. Existing systems often fall short due to their reliance on generic and insufficient measurements. To address these limitations, this paper presents an innovative application designed to overcome these restrictions. The proposed approach utilizes a novel neural network-based model to enhance accuracy. By leveraging well-defined pre-built datasets from trustworthy sources, our model can provide precise and personalized recommendations. Preliminary results indicate an 80% improvement in recommendation accuracy. Additionally, our comprehensive user management system allows users to manage their personal data, while the store management system enables store owners to manage their stores and products. This ensures the application’s adaptability across the clothing industry and its diverse customer base.