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Fashion Product Recommendation Systems: A Machine Learning Perspective
Abstract
A comprehensive fashion product image dataset comprising 40,000 images was curated, encompassing six major categories such as apparel, accessories, footwear, personal care, and sporting goods, along with 44 subcategories like top wear, shoes, bags, and belts. Preprocessing techniques, including Gaussian Laplace distribution and Contrast Limited Adaptive Histogram Equalization (CLAHE), were employed to enhance image quality by emphasizing key features and improving contrast. Feature extraction was conducted using Particle Swarm Optimization (PSO), which efficiently reduced dimensionality by selecting the most relevant features while preserving critical information. A recommender system was then developed using the Fuzzy C-Means (FCM) clustering approach, enabling personalized and accurate recommendations by leveraging overlapping feature clusters. This integrated framework offers a robust foundation for analyzing and recommending fashion products, addressing the complexities of the fashion domain
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