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Consumers seek a shopping experience that not only understands their unique style preferences but also dynamically adapts to their evolving tastes. The fashion industry is at a crossroads, facing increasing consumer demand for personalization, sustainability and transparency in a rapidly evolving digital marketplace. Traditional retail practices, while rich in tradition and artistry, often struggle to up-to-date with the rapidly, ethically-conscious and technology-driven expectations of today\u2019s consumers. \u201cOutfitAI\u201d is designed to address these challenges by leveraging the power of deep learning to revolutionize the fashion retail experience. By automating the process of background removal in fashion images, using advanced algorithms for personalized product matching, and integrating sustainability filters into the product discovery process, OutfitAI aims to deliver a shopping experience that is not only personalized and engaging, but also aligned with the ethical and environmental values of the contemporary consumer. Unlike existing solutions, OutfitAI uses state-of-the-art semantic segmentation for precise background removal, enabling detailed feature extraction from fashion images. This process enables accurate matching of user-uploaded images with similar fashion items from an extensive database of eco-friendly and ethically produced products sourced from leading e-tailers. Setting itself apart from the current state of the art, OutfitAI places a strong emphasis on ethical data use and privacy, implementing robust measures to ensure user privacy and transparency. It also pioneers the integration of sustainability into the digital fashion discovery process, promoting responsible consumption patterns among users. Through a comprehensive system architecture that combines technical innovation with a commitment to ethics and sustainability, OutfitAI not only addresses the technological needs of the fashion retail industry, but also responds to the growing demand for more responsible and transparent consumer technologies.<\/jats:p>","DOI":"10.1007\/s11042-025-20753-x","type":"journal-article","created":{"date-parts":[[2025,3,20]],"date-time":"2025-03-20T04:28:30Z","timestamp":1742444910000},"page":"40195-40214","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["OutfitAI: shop the outfit with a deep learning-based intelligent expert system"],"prefix":"10.1007","volume":"84","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9510-5758","authenticated-orcid":false,"given":"Emanuele","family":"Balloni","sequence":"first","affiliation":[]},{"given":"Rocco","family":"Pietrini","sequence":"additional","affiliation":[]},{"given":"Emanuele","family":"Frontoni","sequence":"additional","affiliation":[]},{"given":"Adriano","family":"Mancini","sequence":"additional","affiliation":[]},{"given":"Marina","family":"Paolanti","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,3,20]]},"reference":[{"key":"20753_CR1","doi-asserted-by":"crossref","unstructured":"Amanlou A, Suratgar AA, Tavoosi J, Mohammadzadeh A, Mosavi A (2022) Single-image reflection removal using deep learning: a systematic review. 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