Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Nikitha Reddy, K.; Iyer, Lakshmi Shankar; Sophia Shalini, G.B. Title A name given to the resource Pattern Identification and Recommender System Based on Skin Undertone in ApparelA Deep Learning Approach Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems;Volume;1277 LNNS;pp.279-294 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-96-2700-4_21" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-96-2700-4_21</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105011292241?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105011292241?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Nikitha Reddy K., School of Business and Management, Christ University, Bengaluru, India; Iyer L.S., School of Business and Management, Christ University, Bengaluru, India; Sophia Shalini G.B., School of Business and Management, Christ University, Bengaluru, India Description An account of the resource The fashion industry has undergone significant transformations driven by technological advancements, shifting consumer preferences, and the rise of e-commerce. Traditional retail models have been disrupted, giving consumers unprecedented access to information and options, making personalization crucial for success. The industry has also embraced inclusivity, offering diverse clothing lines that cater to various body types, skin tones, and cultural backgrounds. Technology plays a pivotal role in this evolution, enhancing design, manufacturing, marketing, and sustainability practices. Despite these advancements, existing recommendation systems often overlook individual characteristics such as skin undertones and pattern preferences, as well as the dynamic nature of fashion trends. This study aims to address these limitations by developing a novel AI-powered recommendation system that integrates personalized factors with real-time fashion trends. The proposed system will analyze customer data, including browsing history, social media activity, and purchases, to provide accurate and tailored fashion suggestions. By incorporating individual traits and the latest trends, the research seeks to create a more effective and responsive recommendation engine, ultimately enhancing the consumer shopping experience and helping fashion brands stay competitive in a rapidly evolving market. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. Subject The topic of the resource AI-powered recommendation; E-commerce; Fashion trends; Personalization; Skin undertones Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 23673370; ISBN: 978-981962699-1; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online