Yumeng Zou¹ and Shuyu Wang², ¹USA, ²California State Polytechnic University, USA

This paper addresses the critical issue of sustainability within the fast-paced fashion industry, characterized by overconsumption and waste. We propose an innovative solution, the Slow Low Fashion app, which leverages advanced artificial intelligence (AI) technology to analyze users' body measurements, providing personalized, genderinclusive clothing recommendations [4]. This approach aims to minimize overconsumption and reduce the environmental footprint by encouraging more thoughtful purchasing decisions. The core technologies of our program include AI for body ratio analysis, a secure and privacy-compliant data handling framework, and an intuitive user interface designed to enhance user experience. Despite challenges such as ensuring the accuracy of body measurements and maintaining data privacy, we addressed these through rigorous testing and implementing encryption protocols. Experimentation across diverse user scenarios demonstrated the app's effectiveness in reducing unnecessary purchases and promoting sustainability. The significant reduction in clothing waste and increased user satisfaction highlight the potential of Slow Low Fashion as a tool for promoting sustainable fashion consumption. Our project underscores the necessity and feasibility of integrating technology and personalization in addressing the environmental challenges posed by the fashion industry.

Recommendation System, Machine Learning, Artificial Intelligence, Mobile Application