A Novel AI-Driven Recommendation System for Eco-Conscious Consumers

The fashion industry is witnessing a rapid technological shift, with a growing emphasis on sustainability and eco-consciousness. Consumers are displaying heightened awareness of environmental concerns, leading to a surge in demand for ethical and ecologically responsible fashion products and services. AI-based fashion recommendation systems have emerged as a potential solution, aiding consumers in making environmentally sound wardrobe choices. This research investigates the potential of such systems in empowering ecoconscious consumers to build sustainable wardrobes. A novel algorithm, EcoLearnFuse, is introduced. It integrates autoencoders and gradient boosting techniques to enhance recommendation accuracy while promoting eco-friendly fashion selections. Comparative analysis is conducted against existing algorithms using established simulation metrics, encompassing recommendation precision, environmental impact assessment, and computational efficiency. The simulations demonstrate that EcoLearnFuse surpasses existing algorithms, highlighting its effectiveness in guiding users towards eco-conscious fashion decisions. The proposed framework incorporates persuasive design elements to positively influence consumer behavior, aligning with the broader goals of sustainable fashion adoption. This study contributes to the advancement of AI-driven solutions for promoting environmental sustainability within the fashion industry, ultimately fostering a more conscious and responsible consumer culture.