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Science4Fashion: An Autonomous Recommendation System for Fashion Designers

Abstract : In the clothing industry, design, development, and procurement teams have been affected more than any other industry and are constantly under pressure to present more products with fewer resources in a shorter time. The diversity of garment designs created as new products is not found in any other industry and is almost independent of the size of the business. Science4Fashion is a semi-autonomous intelligent personal assistant for fashion product designers. Our system consists of an interactive environment where a user utilizes different modules responsible for a) data collection from online sources, b) knowledge extraction, c) clustering, and d) trend/product recommendation. This paper is focusing on two core modules of the implemented system. The Clustering Module combines various clustering algorithms and offers a consensus that arranges data in clusters. At the same time, the Product Recommender and Feedback module receives the designer’s input on different fashion products and recommends more relevant items based on their preferences. The experimental results highlight the usefulness and the efficiency of the proposed subsystems in aiding the creative fashion process.
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https://hal.inria.fr/hal-03287686
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Submitted on : Thursday, July 15, 2021 - 6:11:02 PM
Last modification on : Friday, August 13, 2021 - 4:29:53 PM
Long-term archiving on: : Saturday, October 16, 2021 - 7:07:12 PM

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Sotirios-Filippos Tsarouchis, Argyrios S. Vartholomaios, Ioannis-Panagiotis Bountouridis, Athanasios Karafyllis, Antonios C. Chrysopoulos, et al.. Science4Fashion: An Autonomous Recommendation System for Fashion Designers. 17th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), Jun 2021, Hersonissos, Crete, Greece. pp.729-742, ⟨10.1007/978-3-030-79150-6_57⟩. ⟨hal-03287686⟩

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